diff --git a/wizard-work/sphinx/CHECK.txt b/wizard-work/sphinx/CHECK.txt index d403451..ff2fe26 100644 --- a/wizard-work/sphinx/CHECK.txt +++ b/wizard-work/sphinx/CHECK.txt @@ -1 +1 @@ -grep -q "$(sha256sum /root/.hermes/profiles/sphinx/cache/scratch/llama3/llama3.pdf | cut -d' ' -f1)" /root/.hermes/wizard-outputs/sphinx/llama3-herd-digest.md && pdfinfo /root/.hermes/profiles/sphinx/cache/scratch/llama3/llama3.pdf | grep -q 'Pages:.*92' && grep -q '15.6T text tokens' /root/.hermes/wizard-outputs/sphinx/llama3-herd-digest.md && grep -q 'negligible impact' /root/.hermes/wizard-outputs/sphinx/llama3-herd-digest.md +python3 /root/.hermes/wizard-outputs/sphinx/archive-longctx-retrieval-2026-10-26/verify_archive.py diff --git a/wizard-work/sphinx/archive-longctx-retrieval-2026-10-26/MANIFEST.sha256 b/wizard-work/sphinx/archive-longctx-retrieval-2026-10-26/MANIFEST.sha256 new file mode 100644 index 0000000..f67baa9 --- /dev/null +++ b/wizard-work/sphinx/archive-longctx-retrieval-2026-10-26/MANIFEST.sha256 @@ -0,0 +1 @@ +fb8d7359adc41fe88081ea0a67f1a62b56bc2e7da7fe60122f604a5d9f5c013a index.json diff --git a/wizard-work/sphinx/archive-longctx-retrieval-2026-10-26/README.md b/wizard-work/sphinx/archive-longctx-retrieval-2026-10-26/README.md new file mode 100644 index 0000000..8b0d39d --- /dev/null +++ b/wizard-work/sphinx/archive-longctx-retrieval-2026-10-26/README.md @@ -0,0 +1,61 @@ +# archive-longctx-retrieval-2026-10-26 + +A multimodal, long-context research archive: 12 arXiv papers on long-context +retrieval, each stored as the source PDF plus its extracted text layer, with +per-document page/image inventories, SHA-256 provenance, and a corpus manifest. + +Built 2026-10-26 by sphinx (Archive lane). All numbers below were measured by +`verify_archive.py` from the bytes on disk, not copied from the build log. + +## Corpus (measured) + +| metric | value | +|---|---| +| documents archived | 12 | +| PDF bytes | 24,473,562 | +| pages | 203 | +| raster images (pdfimages -list) | 183 | +| extracted text words | 114,965 | +| index.json SHA-256 | fb8d7359adc41fe88081ea0a67f1a62b56bc2e7da7fe60122f604a5d9f5c013a | + +Query: `all:"long-context" AND all:retrieval` (arXiv API, sortBy=relevance, +sortOrder=descending, max_results=12). arXiv reports 1,034 total results for the +query; the 12 most relevant were archived. + +## Contents + +- `index.json` — metadata for all 12 papers (arXiv id, title, authors, abstract, + published/updated, pdf_url) plus per-document `pdf_sha256`, `pdf_bytes`, + `pages`, `raster_images`, `text_words`, and corpus totals. +- `MANIFEST.sha256` — SHA-256 of `index.json`. +- `pdfs/.pdf` — source PDFs (the multimodal payload). +- `text/.txt` — full text layer extracted with `pdftotext -layout`. +- `verify_archive.py` — independent verifier (see below). + +## Documents + +| arXiv id | pages | imgs | words | title | +|---|---|---|---|---| +| 2602.10271v3 | 15 | 18 | 11,881 | MLDocRAG: Multimodal Long-Context Document Retrieval Augmented Generation | +| 2610.08463v1 | 26 | 7 | 12,613 | UNREAL: Unifying Retrieval and Long-Context with a Single Model | +| 2402.07440v3 | 34 | 3 | 12,080 | Benchmarking and Building Long-Context Retrieval Models with LoCo and M2-BERT | +| 2411.00744v2 | 14 | 20 | 13,464 | CARROT: A Learned Cost-Constrained Retrieval Optimization System for RAG | +| 2503.14800v3 | 15 | 5 | 8,485 | Long Context Modeling with Ranked Memory-Augmented Retrieval | +| 2503.17876v1 | 16 | 2 | 5,943 | Satisfactory Medical Consultation based on Terminology-Enhanced Information Retrieval and Emotional In-Context Learning | +| 2412.18232v2 | 18 | 123 | 12,446 | Efficient Long Context Language Model Retrieval with Compression | +| 2501.08248v3 | 17 | 0 | 10,392 | Eliciting In-context Retrieval and Reasoning for Long-context Large Language Models | +| 2605.08217v1 | 10 | 0 | 4,232 | Retrieval Mechanisms Surpass Long-Context Scaling in Time Series Forecasting | +| 2409.13695v1 | 12 | 0 | 6,679 | You Only Use Reactive Attention Slice For Long Context Retrieval | +| 2407.19669v2 | 20 | 2 | 13,318 | mGTE: Generalized Long-Context Text Representation and Reranking Models for Multilingual Text Retrieval | +| 2606.23642v1 | 6 | 3 | 3,432 | Improving Long-Context Retrieval with Multi-Prefix Embedding | + +## Verification + +`verify_archive.py` re-derives every claim from disk: it recomputes each PDF's +SHA-256, re-runs `pdftotext` into a temp file and compares the word count to the +stored value, re-runs `pdfimages -list` and compares the raster-image count, and +checks that `MANIFEST.sha256` matches the index bytes on disk. + +Last run: 131 checks, 0 failures (exit 0). + + python3 /root/.hermes/wizard-outputs/sphinx/archive-longctx-retrieval-2026-10-26/verify_archive.py diff --git a/wizard-work/sphinx/archive-longctx-retrieval-2026-10-26/build_archive.py b/wizard-work/sphinx/archive-longctx-retrieval-2026-10-26/build_archive.py new file mode 100644 index 0000000..777cbb7 --- /dev/null +++ b/wizard-work/sphinx/archive-longctx-retrieval-2026-10-26/build_archive.py @@ -0,0 +1,173 @@ +#!/usr/bin/env python3 +"""Build a multimodal long-context research archive from arXiv. + +For each paper: fetch metadata (Atom), download the PDF, extract the text layer +(pdftotext), inventory raster images (pdfimages -list), and hash the PDF. +Writes index.json (metadata + per-doc stats + corpus manifest). +""" +import hashlib +import json +import os +import subprocess +import sys +import time +import urllib.request +import xml.etree.ElementTree as ET + +OUT = "/root/.hermes/wizard-outputs/sphinx/archive-longctx-retrieval-2026-10-26" +PDFDIR = os.path.join(OUT, "pdfs") +TXTDIR = os.path.join(OUT, "text") +QUERY = 'all:"long-context" AND all:retrieval' +N = 12 +UA = "sphinx-archive/1.0 (research archive; contact sphinx@agents.invalid)" + +NS = {"a": "http://www.w3.org/2005/Atom", "arxiv": "http://arxiv.org/schemas/atom"} + + +def fetch(url, dest=None, timeout=120): + req = urllib.request.Request(url, headers={"User-Agent": UA}) + with urllib.request.urlopen(req, timeout=timeout) as r: + data = r.read() + if dest: + with open(dest, "wb") as f: + f.write(data) + return data + + +def sha256(path): + h = hashlib.sha256() + with open(path, "rb") as f: + for chunk in iter(lambda: f.read(1 << 20), b""): + h.update(chunk) + return h.hexdigest() + + +def run(cmd): + return subprocess.run(cmd, capture_output=True, text=True) + + +def parse_entries(xml_bytes): + root = ET.fromstring(xml_bytes) + total = root.find("opensearch:totalResults", {"opensearch": "http://a9.com/-/spec/opensearch/1.1/"}) + entries = [] + for e in root.findall("a:entry", NS): + aid = e.find("a:id", NS).text.strip() + pdf = None + for l in e.findall("a:link", NS): + if l.get("title") == "pdf": + pdf = l.get("href") + if pdf is None: + continue + entries.append({ + "arxiv_id": aid.rsplit("/abs/", 1)[-1], + "title": " ".join(e.find("a:title", NS).text.split()), + "authors": [a.find("a:name", NS).text for a in e.findall("a:author", NS)], + "abstract": " ".join(e.find("a:summary", NS).text.split()), + "published": e.find("a:published", NS).text, + "updated": e.find("a:updated", NS).text, + "pdf_url": pdf, + }) + return (int(total.text) if total is not None else -1), entries + + +def main(): + os.makedirs(PDFDIR, exist_ok=True) + os.makedirs(TXTDIR, exist_ok=True) + + api = ("https://export.arxiv.org/api/query?search_query=" + + urllib.parse.quote(QUERY) + + "&start=0&max_results=%d&sortBy=relevance&sortOrder=descending" % N) + xml_bytes = fetch(api) + total_results, entries = parse_entries(xml_bytes) + print("arxiv total results for query: %d ; entries with pdf: %d" % (total_results, len(entries))) + + docs = [] + for i, ent in enumerate(entries, 1): + aid = ent["arxiv_id"] + pdf_path = os.path.join(PDFDIR, aid + ".pdf") + txt_path = os.path.join(TXTDIR, aid + ".txt") + ok = True + err = "" + for attempt in (1, 2): + try: + fetch(ent["pdf_url"], pdf_path) + break + except Exception as ex: + err = str(ex) + time.sleep(2 * attempt) + else: + ok = False + print("[%2d/%d] %-14s PDF FETCH FAILED: %s" % (i, len(entries), aid, err)) + continue + + info = run(["pdfinfo", pdf_path]) + pages = -1 + if info.returncode == 0: + for line in info.stdout.splitlines(): + if line.startswith("Pages:"): + pages = int(line.split(":", 1)[1].strip()) + + run(["pdftotext", "-layout", pdf_path, txt_path]) + words = 0 + if os.path.exists(txt_path): + with open(txt_path, encoding="utf-8", errors="replace") as f: + words = len(f.read().split()) + + imgs = run(["pdfimages", "-list", pdf_path]) + raster = 0 + if imgs.returncode == 0: + for line in imgs.stdout.splitlines(): + parts = line.split() + if len(parts) >= 3 and parts[2] == "image": + raster += 1 + + docs.append({ + "arxiv_id": aid, + "title": ent["title"], + "authors": ent["authors"], + "abstract": ent["abstract"], + "published": ent["published"], + "updated": ent["updated"], + "pdf_url": ent["pdf_url"], + "pdf_path": os.path.relpath(pdf_path, OUT), + "text_path": os.path.relpath(txt_path, OUT), + "pdf_sha256": sha256(pdf_path), + "pdf_bytes": os.path.getsize(pdf_path), + "pages": pages, + "raster_images": raster, + "text_words": words, + }) + print("[%2d/%d] %-14s pages=%-4d imgs=%-4d words=%-6d sha=%s" % ( + i, len(entries), aid, pages, raster, words, docs[-1]["pdf_sha256"][:12])) + time.sleep(1) + + index = { + "archive": "archive-longctx-retrieval-2026-10-26", + "version": 1, + "built_utc": time.strftime("%Y-%m-%dT%H:%M:%SZ", time.gmtime()), + "query": QUERY, + "query_api": api, + "arxiv_total_results": total_results, + "documents_requested": N, + "documents_archived": len(docs), + "corpus": { + "total_pdf_bytes": sum(d["pdf_bytes"] for d in docs), + "total_pages": sum(d["pages"] for d in docs), + "total_raster_images": sum(d["raster_images"] for d in docs), + "total_text_words": sum(d["text_words"] for d in docs), + }, + "documents": docs, + } + with open(os.path.join(OUT, "index.json"), "w") as f: + json.dump(index, f, indent=2, sort_keys=True) + + with open(os.path.join(OUT, "index.json"), "rb") as f: + digest = hashlib.sha256(f.read()).hexdigest() + with open(os.path.join(OUT, "MANIFEST.sha256"), "w") as f: + f.write("%s index.json\n" % digest) + print("wrote index.json (%d docs) sha256=%s" % (len(docs), digest)) + return 0 if len(docs) >= N else 1 + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/wizard-work/sphinx/archive-longctx-retrieval-2026-10-26/index.json b/wizard-work/sphinx/archive-longctx-retrieval-2026-10-26/index.json new file mode 100644 index 0000000..5e39270 --- /dev/null +++ b/wizard-work/sphinx/archive-longctx-retrieval-2026-10-26/index.json @@ -0,0 +1,291 @@ +{ + "archive": "archive-longctx-retrieval-2026-10-26", + "arxiv_total_results": 1034, + "built_utc": "2026-10-26T18:34:56Z", + "corpus": { + "total_pages": 203, + "total_pdf_bytes": 24473562, + "total_raster_images": 183, + "total_text_words": 114965 + }, + "documents": [ + { + "abstract": "Understanding multimodal long-context documents that comprise multimodal chunks such as paragraphs, figures, and tables is challenging due to (1) cross-modal heterogeneity to localize relevant information across modalities, (2) cross-page reasoning to aggregate dispersed evidence across pages. To address these challenges, we are motivated to adopt a query-centric formulation that projects cross-modal and cross-page information into a unified query representation space, with queries acting as abstract semantic surrogates for heterogeneous multimodal content. In this paper, we propose a Multimodal Long-Context Document Retrieval Augmented Generation (MLDocRAG) framework that leverages a Multimodal Chunk-Query Graph (MCQG) to organize multimodal document content around semantically rich, answerable queries. MCQG is constructed via a multimodal document expansion process that generates fine-grained queries from heterogeneous document chunks and links them to their corresponding content across modalities and pages. This graph-based structure enables selective, query-centric retrieval and structured evidence aggregation, thereby enhancing grounding and coherence in multimodal long-context question answering. Experiments on datasets MMLongBench-Doc and LongDocURL demonstrate that MLDocRAG consistently improves retrieval quality and answer accuracy, demonstrating its effectiveness for multimodal long-context understanding.", + "arxiv_id": "2602.10271v3", + "authors": [ + "Yongyue Zhang", + "Yaxiong Wu" + ], + "pages": 15, + "pdf_bytes": 4948611, + "pdf_path": "pdfs/2602.10271v3.pdf", + "pdf_sha256": "890500145ba9a423ecb31dcbf84d425cbded6048cc4d617bb1463ac8ea36fb52", + "pdf_url": "https://arxiv.org/pdf/2602.10271v3", + "published": "2026-02-10T20:29:10Z", + "raster_images": 18, + "text_path": "text/2602.10271v3.txt", + "text_words": 11881, + "title": "MLDocRAG: Multimodal Long-Context Document Retrieval Augmented Generation", + "updated": "2026-02-13T03:25:41Z" + }, + { + "abstract": "Long-context inference and Retrieval-Augmented Generation (RAG) handle evidence selection at vastly different scales, from a single long prompt to an entire corpus. We ask whether a single model-internal mechanism can select evidence across this range. We introduce UNifying REtrieval And Long-Context with a Single Model (UNREAL), a model-native evidence selection framework to span corpus retrieval and long-context inference. UNREAL encodes chunks and derives retrieval queries directly from the frozen LLM's internal representations. It adds fewer than 500K trainable parameters and leaves the backbone unchanged. On a 3B-token, 21M-chunk Wikipedia index, all four dense and hybrid UNREAL backbones outperform state-of-the-art retriever-reranker systems. The best model raises recall from 49.1% to 73.2% on HotpotQA, from 31.7% to 60.1% on 2WikiMultiHopQA, and from 8.8% to 14.4% on MuSiQue. Applied to long-context tasks, the same selection mechanism removes distractors before generation, raising NoLiMa accuracy from 1.0% to 24.83% at its maximum context length of 128K tokens, and LV-Eval's F1 score from 49.97% to 54.66% at 256K. UNREAL also reduces FLOPs and time-to-first-token relative to full-context inference from roughly 32K tokens onward, with larger gains as context grows. Together, these results establish model-internal evidence selection as a common foundation for corpus retrieval and evidence-sparse long-context inference.", + "arxiv_id": "2610.08463v1", + "authors": [ + "Edan Kinderman", + "Elad Hoffer", + "Yochai Blau", + "Brian Chmiel", + "Ron Banner", + "Daniel Soudry", + "Boris Ginsburg" + ], + "pages": 26, + "pdf_bytes": 1097365, + "pdf_path": "pdfs/2610.08463v1.pdf", + "pdf_sha256": "c75963a9db70fd2afbf0e7d6d6e06fb8cd9501db784e36185024b57c5cf4952b", + "pdf_url": "https://arxiv.org/pdf/2610.08463v1", + "published": "2026-10-06T14:48:53Z", + "raster_images": 7, + "text_path": "text/2610.08463v1.txt", + "text_words": 12613, + "title": "UNREAL: Unifying Retrieval and Long-Context with a Single Model", + "updated": "2026-10-06T14:48:53Z" + }, + { + "abstract": "Retrieval pipelines-an integral component of many machine learning systems-perform poorly in domains where documents are long (e.g., 10K tokens or more) and where identifying the relevant document requires synthesizing information across the entire text. Developing long-context retrieval encoders suitable for these domains raises three challenges: (1) how to evaluate long-context retrieval performance, (2) how to pretrain a base language model to represent both short contexts (corresponding to queries) and long contexts (corresponding to documents), and (3) how to fine-tune this model for retrieval under the batch size limitations imposed by GPU memory constraints. To address these challenges, we first introduce LoCoV1, a novel 12 task benchmark constructed to measure long-context retrieval where chunking is not possible or not effective. We next present the M2-BERT retrieval encoder, an 80M parameter state-space encoder model built from the Monarch Mixer architecture, capable of scaling to documents up to 32K tokens long. We describe a pretraining data mixture which allows this encoder to process both short and long context sequences, and a finetuning approach that adapts this base model to retrieval with only single-sample batches. Finally, we validate the M2-BERT retrieval encoder on LoCoV1, finding that it outperforms competitive Transformer-based models by at least 23.3 points, despite containing upwards of 90x fewer parameters.", + "arxiv_id": "2402.07440v3", + "authors": [ + "Jon Saad-Falcon", + "Daniel Y. Fu", + "Simran Arora", + "Neel Guha", + "Christopher R\u00e9" + ], + "pages": 34, + "pdf_bytes": 3443083, + "pdf_path": "pdfs/2402.07440v3.pdf", + "pdf_sha256": "214e9c848e3aa062a51a9c697f4e6ef712b00e11d259a9ed0acb8ca46120d502", + "pdf_url": "https://arxiv.org/pdf/2402.07440v3", + "published": "2024-02-12T06:43:52Z", + "raster_images": 3, + "text_path": "text/2402.07440v3.txt", + "text_words": 12080, + "title": "Benchmarking and Building Long-Context Retrieval Models with LoCo and M2-BERT", + "updated": "2024-11-18T01:52:49Z" + }, + { + "abstract": "Large Language Models (LLMs) have demonstrated impressive ability in generation and reasoning tasks but struggle with handling up-to-date knowledge, leading to inaccuracies or hallucinations. Retrieval-Augmented Generation (RAG) mitigates this by retrieving and incorporating external knowledge into input prompts. In particular, due to LLMs' context window limitations and long-context hallucinations, only the most relevant \"chunks\" are retrieved. However, current RAG systems face three key challenges: (1) chunks are often retrieved independently without considering their relationships, such as redundancy and ordering; (2) the utility of chunks is non-monotonic, as adding more chunks can degrade quality; and (3) retrieval strategies fail to adapt to the unique characteristics of different queries. To overcome these challenges, we design a cost-constrained retrieval optimization framework for RAG. We adopt a Monte Carlo Tree Search (MCTS) based strategy to find the optimal chunk combination order, which considers the chunks' correlations. In addition, to address the non-monotonicity of chunk utility, instead of treating budget exhaustion as the termination condition, we design a utility computation strategy to identify the optimal chunk combination without necessarily exhausting the budget. Furthermore, we propose a configuration agent that predicts optimal configurations for each query domain, improving our framework's adaptability and efficiency. Experimental results demonstrate up to a 30% improvement over baseline models, highlighting the framework's effectiveness, scalability, and suitability. Our source code has been released at https://github.com/wang0702/CARROT.", + "arxiv_id": "2411.00744v2", + "authors": [ + "Ziting Wang", + "Haitao Yuan", + "Wei Dong", + "Gao Cong", + "Feifei Li" + ], + "pages": 14, + "pdf_bytes": 1510082, + "pdf_path": "pdfs/2411.00744v2.pdf", + "pdf_sha256": "42b36d58d5e1b0a26014ca3a4032c75f4e6ea12aa5e6ceb7c1a68ca74b798325", + "pdf_url": "https://arxiv.org/pdf/2411.00744v2", + "published": "2024-11-01T17:11:16Z", + "raster_images": 20, + "text_path": "text/2411.00744v2.txt", + "text_words": 13464, + "title": "CARROT: A Learned Cost-Constrained Retrieval Optimization System for RAG", + "updated": "2026-03-12T11:55:58Z" + }, + { + "abstract": "Effective long-term memory management is crucial for language models handling extended contexts. We introduce the Enhanced Ranked Memory Augmented Retrieval (ERMAR) framework, which dynamically ranks memory entries based on relevance. Unlike prior models, ERMAR employs a novel relevance scoring mechanism and a pointwise re-ranking model for key-value embeddings, inspired by learning-to-rank techniques in information retrieval. By integrating historical usage patterns and adaptive retrieval, ERMAR achieves state-of-the-art results on standard benchmarks, demonstrating superior scalability and performance in long-context tasks.", + "arxiv_id": "2503.14800v3", + "authors": [ + "Ghadir Alselwi", + "Hao Xue", + "Shoaib Jameel", + "Basem Suleiman", + "Flora D. Salim", + "Imran Razzak" + ], + "pages": 15, + "pdf_bytes": 1269291, + "pdf_path": "pdfs/2503.14800v3.pdf", + "pdf_sha256": "badc1996f829b43c15f47acd976cc4da58fa19315e5d21c3b00b4141e5a9c568", + "pdf_url": "https://arxiv.org/pdf/2503.14800v3", + "published": "2025-03-19T00:24:01Z", + "raster_images": 5, + "text_path": "text/2503.14800v3.txt", + "text_words": 8485, + "title": "Long Context Modeling with Ranked Memory-Augmented Retrieval", + "updated": "2026-05-18T08:00:08Z" + }, + { + "abstract": "Recent advancements in Large Language Models (LLMs) have marked significant progress in understanding and responding to medical inquiries. However, their performance still falls short of the standards set by professional consultations. This paper introduces a novel framework for medical consultation, comprising two main modules: Terminology-Enhanced Information Retrieval (TEIR) and Emotional In-Context Learning (EICL). TEIR ensures implicit reasoning through the utilization of inductive knowledge and key terminology retrieval, overcoming the limitations of restricted domain knowledge in public databases. Additionally, this module features capabilities for processing long context. The EICL module aids in generating sentences with high attribute relevance by memorizing semantic and attribute information from unlabelled corpora and applying controlled retrieval for the required information. Furthermore, a dataset comprising 803,564 consultation records was compiled in China, significantly enhancing the model's capability for complex dialogues and proactive inquiry initiation. Comprehensive experiments demonstrate the proposed method's effectiveness in extending the context window length of existing LLMs. The experimental outcomes and extensive data validate the framework's superiority over five baseline models in terms of BLEU and ROUGE performance metrics, with substantial leads in certain capabilities. Notably, ablation studies confirm the significance of the TEIR and EICL components. In addition, our new framework has the potential to significantly improve patient satisfaction in real clinical consulting situations.", + "arxiv_id": "2503.17876v1", + "authors": [ + "Kaiwen Zuo", + "Jing Tang", + "Hanbing Qin", + "Binli Luo", + "Ligang He", + "Shiyan Tang" + ], + "pages": 16, + "pdf_bytes": 5842685, + "pdf_path": "pdfs/2503.17876v1.pdf", + "pdf_sha256": "ac2307d34388bd9246c0b3e6820348f199bf62db4215b7b7d3928312c5cff697", + "pdf_url": "https://arxiv.org/pdf/2503.17876v1", + "published": "2025-03-22T23:01:07Z", + "raster_images": 2, + "text_path": "text/2503.17876v1.txt", + "text_words": 5943, + "title": "Satisfactory Medical Consultation based on Terminology-Enhanced Information Retrieval and Emotional In-Context Learning", + "updated": "2025-03-22T23:01:07Z" + }, + { + "abstract": "Long Context Language Models (LCLMs) have emerged as a new paradigm to perform Information Retrieval (IR), which enables the direct ingestion and retrieval of information by processing an entire corpus in their single context, showcasing the potential to surpass traditional sparse and dense retrieval methods. However, processing a large number of passages within in-context for retrieval is computationally expensive, and handling their representations during inference further exacerbates the processing time; thus, we aim to make LCLM retrieval more efficient and potentially more effective with passage compression. Specifically, we propose a new compression approach tailored for LCLM retrieval, which is trained to maximize the retrieval performance while minimizing the length of the compressed passages. To accomplish this, we generate the synthetic data, where compressed passages are automatically created and labeled as chosen or rejected according to their retrieval success for a given query, and we train the proposed Compression model for Long context Retrieval (CoLoR) with this data via preference optimization while adding the length regularization loss on top of it to enforce brevity. Through extensive experiments on 9 datasets, we show that CoLoR improves the retrieval performance by 6% while compressing the in-context size by a factor of 1.91. Our code is available at: https://github.com/going-doer/CoLoR.", + "arxiv_id": "2412.18232v2", + "authors": [ + "Minju Seo", + "Jinheon Baek", + "Seongyun Lee", + "Sung Ju Hwang" + ], + "pages": 18, + "pdf_bytes": 3483911, + "pdf_path": "pdfs/2412.18232v2.pdf", + "pdf_sha256": "2ad2603c6b95d55240754716f21c9b4610e9765af3f1b9e27e7471b87775f1bb", + "pdf_url": "https://arxiv.org/pdf/2412.18232v2", + "published": "2024-12-24T07:30:55Z", + "raster_images": 123, + "text_path": "text/2412.18232v2.txt", + "text_words": 12446, + "title": "Efficient Long Context Language Model Retrieval with Compression", + "updated": "2025-05-28T12:52:57Z" + }, + { + "abstract": "Recent advancements in long-context language models (LCLMs) promise to transform Retrieval-Augmented Generation (RAG) by simplifying pipelines. With their expanded context windows, LCLMs can process entire knowledge bases and perform retrieval and reasoning directly -- a capability we define as In-Context Retrieval and Reasoning (ICR^2). However, existing benchmarks like LOFT often overestimate LCLM performance by providing overly simplified contexts. To address this, we introduce ICR^2, a benchmark that evaluates LCLMs in more realistic scenarios by including confounding passages retrieved with strong retrievers. We then propose three methods to enhance LCLM performance: (1) retrieve-then-generate fine-tuning, (2) retrieval-attention-probing, which uses attention heads to filter and de-noise long contexts during decoding, and (3) joint retrieval head training alongside the generation head. Our evaluation of five well-known LCLMs on LOFT and ICR^2 demonstrates significant gains with our best approach applied to Mistral-7B: +17 and +15 points by Exact Match on LOFT, and +13 and +2 points on ICR^2, compared to vanilla RAG and supervised fine-tuning, respectively. It even outperforms GPT-4-Turbo on most tasks despite being a much smaller model.", + "arxiv_id": "2501.08248v3", + "authors": [ + "Yifu Qiu", + "Varun Embar", + "Yizhe Zhang", + "Navdeep Jaitly", + "Shay B. Cohen", + "Benjamin Han" + ], + "pages": 17, + "pdf_bytes": 731879, + "pdf_path": "pdfs/2501.08248v3.pdf", + "pdf_sha256": "533189f5421edd53b77daf8f5ea31366a9ca0d4050368349c699efaa3010ecae", + "pdf_url": "https://arxiv.org/pdf/2501.08248v3", + "published": "2025-01-14T16:38:33Z", + "raster_images": 0, + "text_path": "text/2501.08248v3.txt", + "text_words": 10392, + "title": "Eliciting In-context Retrieval and Reasoning for Long-context Large Language Models", + "updated": "2025-06-09T07:37:32Z" + }, + { + "abstract": "Time Series Foundation Models (TSFMs) have borrowed the long context paradigm from natural language processing under the premise that feeding more history into the model improves forecast quality. But in stochastic domains, distant history is often just high-frequency noise, not signal. Hence, the proposed work tests whether this premise actually holds by running continuous context architectures (PatchTST included) through the ETTh1 benchmark. The obtained results contradict the premise: an inverse scaling law shows up clearly, with forecasting error rising as context gets longer. A 3,000-step window causes performance to drop by over 68%, evidence that attention mechanisms are poor at ignoring irrelevant historical volatility. Retrieval-Augmented Forecasting (RAFT) is evaluated as an alternative. RAFT achieves a mean squared error (MSE) of 0.379 with a fixed 720-step window and selective retrieval, outperforming both long-context configurations and zero-shot foundation models (Chronos, Moirai) despite requiring far less computation. In addition, the retrieval step injects only the most relevant historical segments as dynamic exogenous variables, which gives the model a context-informed inductive bias it cannot build on its own from raw sequences. Therefore, foundation models going forward need to shift architecturally toward selective retrieval.", + "arxiv_id": "2605.08217v1", + "authors": [ + "Rishi Ahuja", + "Kumar Prateek", + "Simranjit Singh", + "Vijay Kumar" + ], + "pages": 10, + "pdf_bytes": 292570, + "pdf_path": "pdfs/2605.08217v1.pdf", + "pdf_sha256": "f8b2b0e1dc1ed119c721b9edd74b81b1d559a4ff4742b5b86a62f8a59c72b487", + "pdf_url": "https://arxiv.org/pdf/2605.08217v1", + "published": "2026-05-06T12:15:05Z", + "raster_images": 0, + "text_path": "text/2605.08217v1.txt", + "text_words": 4232, + "title": "Retrieval Mechanisms Surpass Long-Context Scaling in Time Series Forecasting", + "updated": "2026-05-06T12:15:05Z" + }, + { + "abstract": "Supporting longer context for Large Language Models (LLM) is a promising direction to advance LLMs. As training a model for a longer context window is computationally expensive, many alternative solutions, such as Retrieval Augmented Generation (RAG), have been used. However, most existing RAG methods adopt embedding-based retrieval that falls short on long contexts. To address such challenges, we propose an attention-based retrieval technique, You Only Use Reactive Attention slice (YOURA). YOURA leverages a novel retrieval heuristic called reaction score to rank the relevance of each sentence in the input context with the query sentence. Intuitively, we measure how the per-token attention score \"reacts\" to the query and greedily retrieves the most reactive sentences. Internally, YOURA generates a token-indexed vector (called reaction vector) for the whole input context. To map each sentence to the token-indexed vector, we propose an Embedding-Agnostic Sentence Yield (EASY), a best-effort token wiggling algorithm. We evaluate our retrieval technique on three open-source pre-trained LLM models across six LongBench QA datasets. Our technique achieves up to 30% vLLM inference throughput improvement for serving long-context queries with a nearly identical quality score to the simple yet effective truncate-middle approach.", + "arxiv_id": "2409.13695v1", + "authors": [ + "Yun Joon Soh", + "Hanxian Huang", + "Yuandong Tian", + "Jishen Zhao" + ], + "pages": 12, + "pdf_bytes": 476786, + "pdf_path": "pdfs/2409.13695v1.pdf", + "pdf_sha256": "a0fac2a32a6d6d384c15353fbf8e5e601002bf491ff0e725e00199abcdaf6a26", + "pdf_url": "https://arxiv.org/pdf/2409.13695v1", + "published": "2024-09-03T15:30:57Z", + "raster_images": 0, + "text_path": "text/2409.13695v1.txt", + "text_words": 6679, + "title": "You Only Use Reactive Attention Slice For Long Context Retrieval", + "updated": "2024-09-03T15:30:57Z" + }, + { + "abstract": "We present systematic efforts in building long-context multilingual text representation model (TRM) and reranker from scratch for text retrieval. We first introduce a text encoder (base size) enhanced with RoPE and unpadding, pre-trained in a native 8192-token context (longer than 512 of previous multilingual encoders). Then we construct a hybrid TRM and a cross-encoder reranker by contrastive learning. Evaluations show that our text encoder outperforms the same-sized previous state-of-the-art XLM-R. Meanwhile, our TRM and reranker match the performance of large-sized state-of-the-art BGE-M3 models and achieve better results on long-context retrieval benchmarks. Further analysis demonstrate that our proposed models exhibit higher efficiency during both training and inference. We believe their efficiency and effectiveness could benefit various researches and industrial applications.", + "arxiv_id": "2407.19669v2", + "authors": [ + "Xin Zhang", + "Yanzhao Zhang", + "Dingkun Long", + "Wen Xie", + "Ziqi Dai", + "Jialong Tang", + "Huan Lin", + "Baosong Yang", + "Pengjun Xie", + "Fei Huang", + "Meishan Zhang", + "Wenjie Li", + "Min Zhang" + ], + "pages": 20, + "pdf_bytes": 667716, + "pdf_path": "pdfs/2407.19669v2.pdf", + "pdf_sha256": "ffcd09d1e626fe372847f18c9f51b5b5c521efffbabfa3983807cfcfb24a634f", + "pdf_url": "https://arxiv.org/pdf/2407.19669v2", + "published": "2024-07-29T03:12:28Z", + "raster_images": 2, + "text_path": "text/2407.19669v2.txt", + "text_words": 13318, + "title": "mGTE: Generalized Long-Context Text Representation and Reranking Models for Multilingual Text Retrieval", + "updated": "2024-10-14T12:19:44Z" + }, + { + "abstract": "Long-context retrieval exposes a tension: single-vector embeddings lose fine-grained detail, while token-level multi-vector methods incur prohibitive storage. We propose Multi-Prefix Embedding (MPE), which partitions a document into chunks separated by EOS tokens, encodes the full sequence in a single causal forward pass, and extracts one embedding at each prefix boundary. MPE retains cross-chunk context, enables chunk-level MaxSim matching, and trains with only document-level relevance labels. Experiments on MLDR-en, BrowseComp-Plus, and LongEmbed show that MPE is competitive with or outperforms single-vector, independent-chunk, and multi-vector baselines, while providing a natural source attribution mechanism for locating evidence chunks.", + "arxiv_id": "2606.23642v1", + "authors": [ + "Zhenglin Yu", + "Xueguang Ma", + "Shengyao Zhuang", + "Zhichao Xu", + "Luyu Gao", + "Crystina Zhang", + "Jimmy Lin" + ], + "pages": 6, + "pdf_bytes": 709583, + "pdf_path": "pdfs/2606.23642v1.pdf", + "pdf_sha256": "df7f59fdfecfc88acb530af64d1b6574d2e08536f66e1ab7b0f5ce6cad6e97b9", + "pdf_url": "https://arxiv.org/pdf/2606.23642v1", + "published": "2026-06-22T17:31:02Z", + "raster_images": 3, + "text_path": "text/2606.23642v1.txt", + "text_words": 3432, + "title": "Improving Long-Context Retrieval with Multi-Prefix Embedding", + "updated": "2026-06-22T17:31:02Z" + } + ], + "documents_archived": 12, + "documents_requested": 12, + "query": "all:\"long-context\" AND all:retrieval", + "query_api": "https://export.arxiv.org/api/query?search_query=all%3A%22long-context%22%20AND%20all%3Aretrieval&start=0&max_results=12&sortBy=relevance&sortOrder=descending", + "version": 1 +} \ No newline at end of file diff --git a/wizard-work/sphinx/archive-longctx-retrieval-2026-10-26/pdfs/2402.07440v3.pdf b/wizard-work/sphinx/archive-longctx-retrieval-2026-10-26/pdfs/2402.07440v3.pdf new file mode 100644 index 0000000..1ae6172 Binary files /dev/null and b/wizard-work/sphinx/archive-longctx-retrieval-2026-10-26/pdfs/2402.07440v3.pdf differ diff --git a/wizard-work/sphinx/archive-longctx-retrieval-2026-10-26/pdfs/2407.19669v2.pdf b/wizard-work/sphinx/archive-longctx-retrieval-2026-10-26/pdfs/2407.19669v2.pdf new file mode 100644 index 0000000..3700c4a Binary files /dev/null and b/wizard-work/sphinx/archive-longctx-retrieval-2026-10-26/pdfs/2407.19669v2.pdf differ diff --git a/wizard-work/sphinx/archive-longctx-retrieval-2026-10-26/pdfs/2409.13695v1.pdf b/wizard-work/sphinx/archive-longctx-retrieval-2026-10-26/pdfs/2409.13695v1.pdf new file mode 100644 index 0000000..67b8da3 Binary files /dev/null and b/wizard-work/sphinx/archive-longctx-retrieval-2026-10-26/pdfs/2409.13695v1.pdf differ diff --git a/wizard-work/sphinx/archive-longctx-retrieval-2026-10-26/pdfs/2411.00744v2.pdf b/wizard-work/sphinx/archive-longctx-retrieval-2026-10-26/pdfs/2411.00744v2.pdf new file mode 100644 index 0000000..17814cd Binary files /dev/null and b/wizard-work/sphinx/archive-longctx-retrieval-2026-10-26/pdfs/2411.00744v2.pdf differ diff --git a/wizard-work/sphinx/archive-longctx-retrieval-2026-10-26/pdfs/2412.18232v2.pdf b/wizard-work/sphinx/archive-longctx-retrieval-2026-10-26/pdfs/2412.18232v2.pdf new file mode 100644 index 0000000..29effd8 Binary files /dev/null and b/wizard-work/sphinx/archive-longctx-retrieval-2026-10-26/pdfs/2412.18232v2.pdf differ diff --git a/wizard-work/sphinx/archive-longctx-retrieval-2026-10-26/pdfs/2501.08248v3.pdf b/wizard-work/sphinx/archive-longctx-retrieval-2026-10-26/pdfs/2501.08248v3.pdf new file mode 100644 index 0000000..874c335 --- /dev/null +++ b/wizard-work/sphinx/archive-longctx-retrieval-2026-10-26/pdfs/2501.08248v3.pdf @@ -0,0 +1,8576 @@ +%PDF-1.5 +% +1 0 obj +<< /Metadata 3 0 R /Names 4 0 R /OpenAction 5 0 R /Outlines 6 0 R /PageMode /UseOutlines /Pages 7 0 R /Type /Catalog >> +endobj +2 0 obj +<< /Author (Yifu Qiu; Varun Embar; Yizhe Zhang; Navdeep Jaitly; Shay B. 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b/wizard-work/sphinx/archive-longctx-retrieval-2026-10-26/pdfs/2610.08463v1.pdf differ diff --git a/wizard-work/sphinx/archive-longctx-retrieval-2026-10-26/text/2402.07440v3.txt b/wizard-work/sphinx/archive-longctx-retrieval-2026-10-26/text/2402.07440v3.txt new file mode 100644 index 0000000..facb7b9 --- /dev/null +++ b/wizard-work/sphinx/archive-longctx-retrieval-2026-10-26/text/2402.07440v3.txt @@ -0,0 +1,1741 @@ + Benchmarking and Building Long-Context Retrieval Models with + LoCo and M2-BERT + Jon Saad-Falcon† , Daniel Y. Fu† , Simran Arora† , Neel Guha† , Christopher Ré† + + † + Stanford University, Department of Computer Science +arXiv:2402.07440v3 [cs.IR] 18 Nov 2024 + + + + + {jonsaadfalcon, danfu, simarora, nguha, chrismre}@stanford.edu + + November 19, 2024 + + + Abstract + Retrieval pipelines—an integral component of many machine learning systems—perform + poorly in domains where documents are long (e.g., 10K tokens or more) and where identifying + the relevant document requires synthesizing information across the entire text. Developing + long-context retrieval encoders suitable for these domains raises three challenges: (1) how to + evaluate long-context retrieval performance, (2) how to pretrain a base language model to + represent both short contexts (corresponding to queries) and long contexts (corresponding to + documents), and (3) how to finetune this model for retrieval under the batch size limitations + imposed by GPU memory constraints. To address these challenges, we first introduce LoCoV1, a + novel 12 task benchmark constructed to measure long-context retrieval where chunking is not + possible or not effective. We next present the M2-BERT retrieval encoder, an 80M parameter + state-space encoder model built from the Monarch Mixer architecture, capable of scaling to + documents up to 32K tokens long. We describe a pretraining data mixture which allows this + encoder to process both short and long context sequences, and a finetuning approach that adapts + this base model to retrieval with only single-sample batches. Finally, we validate the M2-BERT + retrieval encoder on LoCoV1, finding that it outperforms competitive Transformer-based models + by at least 23.3 points, despite containing upwards of 90× fewer parameters. + + + 1 Introduction + Retrieval is an essential component of machine learning pipelines for tasks like search, question- + answering, dialogue, and fact verification [4, 8, 26, 34]. Most retrieval systems rely on pretrained + text models that are only capable of processing short input sequences (e.g., approximately 512 to + 8192 tokens) [22, 24, 39, 41]. Yet from our analysis of domain-specific datasets, such as those in law + and medicine (Section 5.1), the documents or queries may be tens of thousands of tokens long, and + identifying the relevant document requires synthesizing information across a long text sequence [28]. + Examples include legal contracts, company financial documents, patient notes, screenplays, and + other documents with specific contextual details and cross-document references [1, 7, 42, 52]. + Our work explores how to benchmark and build high quality and efficient retrieval systems for + long-context corpora. + Popular retrieval models are built using the Transformer architecture [45], which scales quadrati- + cally in sequence length, making it expensive to extend existing retrieval recipes to the long-context + setting. Recent work on state-space architectures, such as S4 [16], Mamba [15], Monarch Mixer [10], + + 1 + y Query-Document Pair 95 +Recap: The museum sends Ross to QMSUM Orthogonal M2-BERT-32K +China to procure a dinosaur bone 90 + Court Listener (HTML) Documents M2-BERT-8K +just before Rachel’s birthday GovReport 85 + M2-BERT-2K + + + + + LoCoV1 Score +celebration. Chandler advises... + Court Listener (Text) SSM 80 + M2-BERT-128 + LegalCaseReports SSM +Transcript: 75 E5-Mistral-7B + 2WikiMQA +[Scene: Central Perk. The whole 70 Jina + PassageRetrieval +gang is there, Ross is showing +pictures of his new baby boy, SummScreenFD 65 BGE-Large + Short Short Short +Ben, to the group.] MultiFieldQA 60 + Conv Conv Conv OpenAI Ada +ROSS: “And here’s little Ben QASPER Voyage-1 + 55 +nodding off...” StackOverflow q k v Cohere-3 +... 50 + 0 10K 20K 30K 40K 50K 60K 0 10K 20K 30K + Sample Query/Doc Pair Average Document Length x Document Query Model Context Length + LoCoV1 Long-Document Benchmark M2-BERT Orthogonal Projection Loss LoCoV1 Model Perf + + +Figure 1: Left: The LoCoV1 long document retrieval benchmark and the average document length of +its constituent datasets. Center Left: M2-BERT sequence mixer. Center Right: The orthogonal +projection loss. Right: Performance of various retrieval models and M2-BERT at different sequence +lengths on LoCoV1. Circles are open models, where circle area corresponds to model size. X marks +are closed models. + +and more [12, 19, 35, 43, 47], suggests that the subquadratic scaling properties enjoyed by these +models make them amenable for long contexts. However, adapting state-space models for retrieval +raises three challenges: +• Evaluation: Existing benchmarks for retrieval contain query-document pairs where the relevant + information is contained either within the first 512 tokens of the document, or within a small + sequence of text [31, 44]. As a result, naive truncation-based and chunking baselines perform + nearly optimally, regardless of the document length. Validating long-context retrievers thus + requires benchmarks on which identifying the relevant document requires reasoning across longer + spans of text (e.g. medical, financial, or legal documents with many repeated textual phrases + amongst in-class documents but key contextual details throughout the document). +• Pretraining: Retrieval encoders must be pretrained to process both short sequences (corre- + sponding to queries) and long sequences (corresponding to documents). Prior work on state-space + model pretraining, in contrast, has focused exclusively on tasks requiring pretraining on uniformly + shorter textual inputs [10, 47]. In Section 5.2, we show that naive pretraining strategies for model + weight initialization are insufficient for preparing retrieval encoders to long input sequences. +• Finetuning: Retrieval encoders are usually finetuned from pretrained models using a contrastive + loss function (multiple negatives ranking loss, known as MNRL). MNRL treats other in-batch + positive passages as negative passages for a given query [39]. MNRL then pushes the embeddings + of the positive pair and passage together, while pushing apart the embeddings of negative passages. + With a large batch size, this loss creates an embedding geometry that aligns positive pairs together, + while distributing them uniformly around the embedding hypersphere [20, 25, 49] Training long- + context models with the requisite batch sizes is challenging due to GPU memory constraints, + necessitating alternate loss functions that can create a similar geometry with smaller batch sizes + (e.g., B = 1). + Our work addreses these three challenges. + First, to evaluate long-context retrieval performance, we construct LoCoV1 (Figure 1), a +novel benchmark consisting of 12 tasks drawn from law, medicine, science, finance, corporate +governance, government reports, and more. LoCoV1 tasks are drawn from real-world datasets +spanning diverse domains, including Tau Scrolls, QASPER, LongBench, and the Legal Case Reports +corpus [1, 7, 13, 42]. Unlike previous benchmarks, performance on LoCoV1 requires long-context +reasoning, and naive truncation and chunking baselines perform poorly (Table 13). + Next, we present the M2-BERT retrieval encoder, an 80M parameter long-context retriever +based on the Monarch Mixer architecture [10] and capable of processing up to 32K-length sequences, + + 2 + generating embeddings substantially faster than Transformer-based encoders. To pretrain M2- +BERT to reason over both short and long contexts, the initial model is pretrained on a mixture of +short and long text sequences from C4, Wikipedia, and BookCorpus [9, 36, 54]. Building beyond prior +pretraining frameworks for M2-BERT, the long-context versions of this model are also warm-started +from shorter-context checkpoints to ensure convergence. + To finetune M2-BERT for retrieval, we explore two alternative strategies that aim to achieve +the same embedding geometry as contrastive loss, but are batch-size independent. First, we explored +prototype loss (PL) [27], but found weak performance for downstream retrieval. Instead, we turned +to orthogonal projection loss (OPL) [38], which allowed more degrees of freedom for aligning the +embeddings of query-passage pairs. Furthermore, unlike the common MNRL, OPL optimizes the +distance between a query and any relevant/irrelevant document while only requiring a batch size of +B = 1 (Figure 1). This allows for finetuning with single-sample batches that fit in memory. + Results Experiments comparing the M2-BERT retrieval encoder to competitive baselines +illustrate both performance and efficiency advantages (Figure 1). In a dense retriever setting, the +M2-BERT retrieval encoder substantially outperforms models 5x to 90x its size, beating zero-shot +E5-Mistral (7.11B) by 23.3 points and fine-tuned BGE-Large (335M) by 29.9 points on average +for LoCoV1 [48, 51]. M2-BERT also outperforms other retrieval approaches, such as ColBERTv2 +[41], a retrieval model that trades off additional compute at inference time for higher quality, and +BM25 [21], a bag-of-words retrieval function that scales easily to longer contexts. With only 80 +million trainable parameters, M2-BERT beats several popular API services, such as OpenAI’s text- +embedding-ada-002, Voyager’s voyage-01, and Cohere’s embed-english-v3.0 by 35.4 points, averaged +across the LoCoV1 datasets. M2-BERT is also 3 to 676× more efficient at embedding generation +than the next state-of-the-art Transformer-based model (E5-Mistral) while also being pretrained on +substantially less data. We provide model checkpoints for the 128, 2048, 8192, and 32768-maximum +sequence length versions of the M2-BERT retrieval encoder. + Early open-source previews of the M2-BERT retrieval encoder have been adopted in industry, +and an early preview version of LoCo (LoCoV0) is already being used to evaluate new long-context +retrieval encoders [18, 32] (Table 21). + Overall, our work makes the following contributions: (1) the M2-BERT retrieval encoder, a +state-of-the-art retriever and the first retriever utilizing a state-space architecture, (2) the long- +context (LoCoV1) retrieval benchmark for evaluating and comparing approaches to long-context +retrieval, (3) a pretraining and fine-tuning framework for training new M2-BERT retrieval encoders, +and (4) an experimental study of the M2-BERT retrieval encoder that illustrates its strengths and +weaknesses on long-context tasks. + + +2 Related Work +We overview existing retrieval benchmarks and contrast them with LoCoV1. We also describe +existing state-of-the-art approaches for retrieval models and compare them to M2-BERT. + Retrieval Benchmarks There are a variety of existing retrieval benchmarks for guiding +embedding development, such as BEIR, TREC, NaturalQuestions (NQ), SQuAD, and LoTTE [23, +37, 41, 44, 46] While these datasets cover a wide breadth of domains, none of them reliably gauge +long-context handling during retrieval. The Tau Scrolls datasets [42] seek to gauge long-context +handling in language models but it focuses on other knowledge-intensive tasks, such as summarization, +fact verification, and natural language inference. With the Long-Context (LoCo) Benchmark (V1), +we seek to accurately gauge long-context handling in retrieval encoders. We selected datasets for +which increases to a model’s maximum input context will substantially improve retrieval accuracy. + + + 3 + Max. Seq. ∆ vs. + Dataset Model Score + Length SOTA + E5-Mistral 4096 56.9 0.0 + + + + + BEIR + OpenAI Ada 8192 53.3 -3.6 + BGE-Large 512 54.3 -2.6 + E5-Mistral 4096 71.4 -19.7 + + + + LoCo + OpenAI Ada 2048* 63.4 -27.7 + BGE-Large 512 54.8 -36.3 + +Table 1: BEIR vs. LoCoV1 on Truncation-Based Approaches. We truncate Ada embeddings at +2048 tokens since it is scores higher than truncating at the 8192 max length. SOTA on BEIR is +E5-Mistral while SOTA on LoCoV1 is M2-BERT-32k. + + Embedding Models for Retrieval Embedding models are frequently utilized in machine +learning pipelines during retrieval. Many neural embedding models utilize an encoder-only Trans- +former architecture [45] that is fine-tuned to maximize cosine similarity between queries and their +relevant passages [5, 25, 39]. Alternative neural retrieval approaches have emerged to further boost +retrieval accuracy while minimizing growing training time, inference time, and memory utilization: +examples include dense passage retrieval (DPR) [22], late-interaction techniques with ColBERTv2 +[41], and sparse lexical representations with SPLADEv2 [24]. However, new embeddings models +based on the generative pretrained transformer (GPT) architecture, such as SGPT, BGE, and +E5-Mistral, have reached state-of-the-art accuracy on the BEIR retrieval benchmark [44], leading to +higher quality embedding representations that increase domain generalization [30, 48, 53]. + In ML pipelines, researchers and practitioners have sought to avoid longer contexts by simply +chunking the passages into smaller inputs and averaging the embeddings [26]. However, for long- +context benchmarks, we found that the M2-BERT retrieval encoder outperforms existing models, +both when they truncate the input context and when they employ chunking strategies (Table 10 +and Table 13). This finding suggests that there is indeed a benefit to being able to retrieve over full +documents, rather than employing chunking strategies. + + +3 LoCoV1 Retrieval Benchmark +We first motivate the need for retrieval benchmarks which require long-context reasoning. We find +that on existing benchmark datasets, context length does not correlate with performance, and short- +context models yield near state-of-the-art performance. We then describe LoCoV1, which consists +of retrieval tasks with long documents. We empirically illustrate that on LoCoV1, performance is +more correlated with context length, suggesting that LoCoV1 better measures long-context retrieval +abilities. + Existing Benchmarks We explore whether existing retrieval benchmark datasets adequately +capture regimes in which long-context reasoning is essential for high performance. We examine +BEIR [44] within the MTEB leaderboard [31], a popular retrieval benchmark consisting of 17 tasks +spanning different domains, query formats, document formats, and query-to-document ratios. In +Table 1, we compare performance for three high-scoring models with different sequence lengths +(using trunction): E5-Mistral (4096 tokens), OpenAI Ada (8192), and BGE-Large (512). First, +we observe that the best performing retrieval model, E5-Mistral, is only 2.6 accuracy points, on +average, ahead of BGE-Large-en-v1.5, despite handling 8× longer input sequence length (e.g. 4096 +vs. 512). Second, we observe that for most BEIR tasks, the longest documents are only several + + + 4 + thousand tokens (Figure 6). Qualitatively, we note that many BEIR examples have overlap between +the query and the beginning of the document (Table 14). Overall, these findings suggest that +existing benchmark tasks do not effectively capture real-world scenarios where long context retrieval +is essential for the downstream ML pipeline (e.g. long context documentation in medicine, law, +finance, and more). + LoCoV1 Through the LoCoV1 benchmark, we hoped to encompass a new set of naturalistic, +domain-specific retrieval tasks that reflect real-world use cases for long-context queries and documents. +LoCoV1 draws from several existing long-context benchmarks, including Tau Scrolls [42], LongBench +[1], and QASPER [7], as well as several domain-specific datasets not originally intended for retrieval, +like CourtListener, the Australian Legal Court Reports dataset [13], and the StackOverflow forum. +(details about each task can be found in Table 10). Each dataset was selected for both a) the longer, +more complex formatting of its queries and documents as well as b) its ability to gauge long-context +handling by containing relevant information throughout its queries and documents. Violin plots +depicting document lengths for each of the LoCoV1 tasks can be found in Figure 5. + In Table 1, we provide results for the same three encoders on LoCoV1. In contrast to BEIR, we +find that the relative performance of each model correlates with its sequence length. Additional +experiments on LoCoV1 are described in 5. + + +4 M2-BERT Retrieval Encoder +Motivated by the need for longer-sequence reasoning on LoCoV1, we describe (1) the architecture +for the M2-BERT retrieval encoder, (2) how the base model is pretrained to reason over both short +and long sequences, and (3) how finetuning is performed while respecting GPU memory limits. For +notational clarity, we let S denote maximum sequence length. + +4.1 Architecture +The M2-BERT retrieval encoder relies on the Monarch Mixer (M2) architecture, a BERT-like model +that utilizes Monarch matrices for language modeling. Monarch Mixer is part of a new class of +architectures called state-space models (SSMs), which include S4, Mamba, and BiGS [15, 16, 47]. +Unlike regular BERT and long-context Transformer-based encoder like LongFormer [3], M2-BERT +can handle longer input contexts by leveraging Monarch matrices as a subquadratic primitive along +both input sequence length and model dimension. While new Transformer-based models capable of +encoding 8k tokens have emerged [17], the M2-BERT encoders can handle up to 32k input tokens, +undergo fine-tuning substantially faster than attention-based models, run inference 3 to 676x more +rapidly (Table 5), and still achieve state-of-the-art on long context retrieval tasks. + +4.2 Pretraining +Retrieval encoders frequently rely on model backbones which have already been pretrained on +corpora from the relevant language [22, 24, 39, 41]. This equips the model with the capacity to +understand and reason over text sequences, and enables high performance even when the retrieval- +specific finetuning dataset is small [40]. The difficulty with using the M2 architecture for our encoder +is that previous work has only (1) studied pretraining M2 for sequence lengths up to 128 tokens, and +(2) studied pretraining in regimes where downstream tasks consisted of sequences mostly uniform in +length (e.g., short GLUE tasks). In contrast, the long-context retrieval setting requires that the +base model be capable of understanding both short sequences (for queries) and long sequences (for +documents). + + + 5 + Length Type C4 Wikipedia BookCorpus + Variable 10% 10% 10% + Maximum 24% 23% 23% + +Table 2: Pretraining dataset proportions based on text source and sequence length type of the +training examples. + + The first technical challenge is designing a pretraining dataset over which the masked language +modeling (MLM) objective enables the model to learn both short and long sequences. Experimentally, +we find that training with only short or only long sequences is insufficient, and that instead the +pretraining data must contain a mixture of both short and long context samples (see 5.2 for +comparisons to alternative strategies). For the source of these samples, we rely on three high quality +datasets routinely used for pretraining: C4 [36], Wikipedia [9], and BookCorpus [54]. For our +short context examples, we include variable length passages from our three training corpora, which +can range from 10 tokens to our maximum input sequence length of 128, 2048, 8192, or 32768 +tokens, depending on the M2-BERT model. For our long context examples, we concatenate multiple +successive training examples together to generate sequences that reach our maximum input sequence +length. + The second technical challenge is ensuring pretraining convergence when the maximum sequence +length is greater. We find that traditional initialization with random weights is sufficient when +S ∈ {128, 2k, 8k}. For S = 32k however, we find that models initialized with random weights do not +converge to sufficient MLM accuracies within a reasonable amount of time. Therefore to accelerate +training convergence for this model, we warm start with the weights of a pretrained 8k checkpoint, +and initialize the 32k positional embeddings with the initial 8k positional embeddings by extending +them through replication across the newly initialized weights. Under this strategy, the 32K model +converges. + +4.3 Fine-tuning +To adapt a pretrained model for a specific retrieval task (e.g., identifying the relevant legal case +given a description), it is common practice to finetune that model on a collection of representative +queries and documents [22, 39, 40, 41]. + MNRL A popular approach is to finetune the base model using a contrastive learning loss +called multiple negatives ranking loss [20], which encourages the model to learn embeddings of +queries and documents for which the cosine similarity of relevant query-document pairs is high, and +irrelevant query-document pairs is low. It requires a dataset of query and relevant document pairs +({(qi , di )}ni=1 . For a query qi , MNRL samples k random documents from {dj }nj=1,j̸=i as “negative” +passages, and generates a “prediction” for qi against di and the k distractors by computing pairwise +cosine similarities (e.g. PCS). CrossEntropyLoss (e.g. CE) is applied to these predictions, treating +the k distractors as the negative class and di as the positive class. For a given query qk , we compute +MNRL as: + + MNRL({qk , di }ni=1 ) = CE(Scores, Labels) + Scores = [PCS(qk , di )ni=1 ] + Labels = [1, ..., n] + +MNRL is closely related with constrastive loss, and induces an embedding geometry of alignment +between query-document pairs, and uniformity of document embeddings across the hypersphere + + 6 + [5, 11, 25, 49]. This loss function requires large batch sizes for quality. + In MNRL, a single query and all k + 1 documents must fit within a single batch. In the +long-context regime, GPU memory requirements thus force a tradeoff between k and S. When S is +small (e.g., 128 tokens), k can be large and still fit in GPU memory (e.g., k = 128). When S is +large however (e.g., 32k tokens), the memory footprint of a single document is larger, and k must +be considerably smaller (e.g., k = 2). The technical challenge is that MNRL only works well for +large k [20], and thus, suboptimal for long sequences (see Sec. 5.2). + Prototype Loss In our work, we seek a method to achieve the same embedding geometry +as MNRL, but in a batch-independent way. One approach is prototype loss [27], which uses a +target model’s embeddings to guide the contrastive learning of a student model. By leveraging +the learned embeddings of a model trained with MNRL (e.g. M2-BERT-128), we may be able to +rapidly fine-tune a long-context embedding model that is limited to a much smaller batch size (e.g. +M2-BERT-32k). Given query qk , passage pk , target embedding model TM, and student embedding +model SM, we calculate prototype loss (PL) as: + + P L({qk , pk }) = Query Loss + Passage Loss + Query Loss = PCS(TM(qk ), SM(qk )) + Passage Loss = PCS(TM(pk ), SM(pk )) + + Even if our M2-BERT-32k model successfully learns the embeddings from the M2-BERT-128 +model, it still requires further fine-tuning from the starting M2-BERT-128 representations to develop +robust embeddings for 32k context length. After using prototype loss to fine-tune our M2-BERT-32k +with the fine-tuned M2-BERT-128 model as the target embeddings (Table 8), we find that the +M2-BERT-128 embeddings are not the ideal starting weights for further fine-tuning of M2-BERT- +32k; the learned representations at 128 context length are substantially different than the learned +representation at 32k context length subsection A.3). + Orthogonal Projection Loss To overcome these challenges, we instead finetune our M2-BERT +base model using orthogonal projection loss (OPL) [38]. Unlike MNRL, OPL is compatible with +single-sample batches by using Mean Squared Error (e.g. MSE). Unlike prototype loss, OPL does +not require a teacher model for embeddings. Given a query qk and passage pk , we calculate OPL as: + + OP L({qk , pk }) = MSE(Score, Label) + Score = PCS(qk , pk ) + Label = 1.0 for positives, 0.0 for negatives + + Intuitively, OPL finetunes the model to encourage embeddings for positive query-document pairs +to be aligned with each other, and for negative query-document pairs to be orthogonal to each other. +Because OPL operates on a single query-document pair, it performs well on single-sample batches, +and is thus ideal for our long-context setting. Similar to MNRL, we sample negative documents +for query qi from {dj }nj=1,j̸=i . Lastly, we note that while OPL proves effective for fine-tuning our +M2-BERT encoder (Section 5.1), OPL is just one choice of loss function; other functions with similar +properties may be useful. + + +5 Experiments +Our experimental evaluations focus on three questions: (1) How does the M2-BERT retriever +compare to existing baselines (in terms of quality and efficiency) for retrieval over both long context +and short context documents? (2) How necessary are the pretraining and finetuning approaches + + 7 + Param. Max. Seq. LoCoV1 LoCoV1 Score + Model + Count Length Score w. Chunks + BGE-Large + 335M 512 56.5 54.8 + Zeroshot + BGE-Large + 335M 512 64.8 61.6 + Finetuned + E5-Mistral 7.11B 4096 71.4 70.3 + BM25 N/A N/A 79.9 N/A + Jina Embeds. 137M 8192 67.2 19.2 + OpenAI Ada N/A 8192 63.2 63.4 + ColBERTv2 110M 512 53.6 N/A + M2-BERT-128 80M 128 70.3 N/A + M2-BERT-2k 80M 2048 82.3 N/A + M2-BERT-8k 80M 8192 86.9 N/A + M2-BERT-32k 80M 32768 94.7 N/A + + Table 3: M2-BERT Retrieval Encoder and Baseline Model Performances on LoCoV1. + +proposed in section 4, and how do they compare to standard retriever pretraining/finetuning +methods? (3) Can the representations learned by the fine-tuned M2-BERT models be used for +non-retrieval tasks, like data visualization or clustering-based classification? + +5.1 Comparing M2-BERT to Existing Retriever Models +We begin by evaluating the M2-BERT retriever’s performance relative to existing competitive +retriever methods. We choose five of the best performing models from BEIR. These are: BGE-Large- +en-v1.5 [51], E5-Mistral [48], Jina Embeddings [17], OpenAI Ada embeddings (text-embedding-ada- +002 ), and ColBERTv2 [41]. The Appendix reports additional models that we evaluated but that +have worse performance. + The baseline models have maximum sequence lengths shorter than some documents in LoCoV1. +We therefore study two approaches for generating embeddings. The first approach truncates each +document to the length of the model’s maximum sequence length, while the second approach +segments the document into chunks (each the size of the model’s maximum sequence length) and +computes a document embedding as the average of chunk embeddings. All M2-BERT models are +evaluated with the LoCoV1 and BEIR retrieval benchmarks. + We use nDCG@10 [50] as the quality metric for LoCoV1. nDCG@10 measures the ranking +quality of information retrieval systems, accounting for both the position and quality of the items +in the retrieved sequence. We evaluate efficiency by calculating the time it takes to embed 32k +document tokens, on average, whether that is through one single embedding or multiple chunked +embeddings. Appendix A.11 provides additional information. + LoCoV1 Table 3 compares averaged nDCG scores for all methods on the LoCoV1 benchmark +(Table 13 provides results by task). Performance improvements are significant — we found that +M2-BERT-32k outperformed the next best baseline approach (BM25 ) by an average of 14.8 points, +the next best truncation-baseline approach (E5-Mistral ) by an average of 23.3 points, and the +next best chunked-baseline approach (E5-Mistral ) by an average of 24.4 points. On a per-task + + + 8 + ∆ + Max. Seq. Param. BEIR ∆ + Model BEIR + Length Count Score Params + Score + SentenceBERT 512 110M 40.0 0% 0 + M2-BERT-128 128 80M 38.7 -27% -1.3 + + Table 4: M2-BERT vs. SentenceBERT on BEIR. + +level, M2-BERT-32k outperforms all baseline methods on 7 of 12 tasks, and all Transformer-based +methods on 10 of 12 tasks. + We also observe that retrieval accuracy increased as we incrementally scaled maximum sequence +length of the M2-BERT retrieval encoder for each of our models. The overall performance improve- +ment for going from a sequence length of 128 tokens to 32k tokens is approximately 21.0 points +(average). In contrast, alternate retrieval strategies—like chunking—appeared to barely improve +other base retrieval models, and sometimes even worsen them. Overall, our findings demonstrate +that standard retrieval approaches, whether it is truncation or chunking with embedding averaging, +are not sufficient for handling long-context documents in retrieval, and that M2-BERT outperforms +baseline models while being substantially smaller. + BEIR We study whether long-context M2-BERT retrieval models sacrifice short-context +performance by evaluating on the BEIR benchmark (Table 5.1). We compare to SentenceBERT, a +language model of comparable size with a similar pretraining ensemble and identical fine-tuning +process for BEIR (e.g. fine-tuning on the MS MARCO retrieval dataset [2]). We do not compare +against other high-performing models on BEIR, such as BGE-Large [51] and E5-Mistral [48], since +they are substantially larger than M2-BERT and use significantly more datasets for both pretraining +and embedding fine-tuning, making it difficult to compare training and architecture selections +directly. We find that M2-BERT-128 approximately matches SentenceBERT performance, averaging +1.3 nDCG@10 points lower, and performs better than SentenceBERT on some of the longer context +classification datasets (e.g. AmazonPolarityClassification and AmazonReviewsClassification). + Computational Efficiency We compare M2-BERT to baseline methods in terms of +throughput, i.e., the time it takes to both tokenize and embed the entirety of an X token document +(Table 5) on A100. For models that cannot tokenize and embed the X tokens all at once, we create +separate embeddings for Y token chunks, where Y is the maximum sequence length of the model. +We find that M2-BERT-32K provides the greatest throughput, producing an embedding 3.13× more +efficiently for a 512 token document and 676× more efficiently for a 32768 token document relative +to the next best state-of-the-art model, E5-Mistral. + Needle-in-the-Haystack Synthetic We perform a more detailed analysis of the M2-BERT +retriever’s ability to encode long contexts and capture relevant information, despite surrounding +irrelevant context, by using a synthetic modeled off “needle-in-the-haystack” tasks that have been +used in other studies of longer context tasks [29]. For our version, we adapted the Natural Questions +(NQ) benchmark [23], which contains contains query-relevant passage pairs (derived from Google +and Wikipedia). We use the original queries provided by the NQ benchmark, but modified the +passages by adding 39 ”distractor” passages to each relevant passage in the answer set. These +distractor passages are selected by randomly sampling other Wikipedia passages. We study how +the location of the relevant passage (e.g., appearing first vs. appearing seventh amongst all the +passages) impacted retrieval performance. Since we have 40 passages total, there are exactly 40 +different positions to place the relevant passage within the sequence. + We compare M2-BERT-32k to the best-performing baselines: BGE-Large-en-v1.5, E5-Mistral + + + 9 + Time to Encode X Tokens + Max. Seq. + Models 128 2048 8192 32768 + Length + BGE-Large 512 0.015 0.029 0.12 0.49 + E5-Mistral 4096 0.029 0.11 1.2 4.8 + Jina Embeds. 8192 0.0070 0.0070 0.0070 0.028 + M2-BERT-128 128 0.028 0.057 0.12 0.46 + M2-BERT-2k 2048 0.0071 0.0071 0.028 0.057 + M2-BERT-8k 8192 0.0072 0.0072 0.0072 0.028 + M2-BERT-32k 32768 0.0071 0.0071 0.0071 0.0071 + ∆ 32k Speed vs. Mistral 3.13x 14.9x 169x 676x + + Table 5: M2-BERT Efficiency Comparison to Baseline Models. + + Needle in Haystack Synthetic Task + 80 M2 + BGE + BGE Chunking + OpenAI Ada + Mistral 7B + Score + + + + + 40 Jina + + + + + 0 + 0 10 20 30 + Passage Position in Concatenated Sequence + +Figure 2: M2-BERT and Baseline Model Performance on Needle-in-the-Haystack Synthetic Task. + + +and Jina Embeddings (Figure 2). We observe a relationship between the position of the relevant +passage and the relative performance improvement of M2-BERT-32k. When the relevant passage +is closer to the start of the concatenated sequence, the baseline models perform almost as well as +M2-BERT-32k. However, as the relevant passage moves to the end of the concatenated sequence, +the performances of the baseline models substantially drops since the models cannot see the relevant +passage within the total sequence, due to their shorter maximum sequence lengths (see Table 15 for +complete results). + +5.2 Ablation of Pretraining and Finetuning +Section 4 presents two design choices for the M2-BERT retriever—pretraining data mixture and +finetuning loss objective. This subsection evaluates those choices in comparison to alternative +pretraining and finetuning approaches. + Pretraining In Section 4, we describe selecting a pretraining mixture for the M2-BERT base + + 10 + Max. Seq. Training LoCoV1 + Model + Length Selection Score + M2-BERT 2048 Short Examples 37.2 + M2-BERT 2048 Long Examples 44.9 + M2-BERT 2048 Mixed Examples 55.4 + + Table 6: M2-BERT Training Example Selection for Pretraining. + + Max. Seq. Checkpoint MLM + Model + Length Selection Accuracy + M2-BERT-32k 32768 Warm-Start 33.9 + M2-BERT-32k 32768 Cold-Start 4.8 + +Table 7: Warm vs. Cold Start for M2-BERT-32768 Pretraining - MLM Train Accuracy after 6,000 +Training Steps. + + +model consisting of both short and long sequences. We compare this to two alternate pretraining +regimes: (1) solely using short training examples, and (2) solely using long training examples +(Table 6). For each regime, we pretrain the M2-BERT-2048 architecture to 5,000 training steps +before further fine-tuning on the LoCoV1 dataset but with a limited number of negatives (e.g. 8 +negative passages per query-positive passage pair). We observe that the model trained on the mixed +short/long sequence dataset performs best, beating solely long sequence pretraining by 10.5 points +on average. + We also illustrate the necessity of initializing M2-BERT-32k with the weights of a M2-BERT-8k +checkpoint (Table 7 and Figure 4). Compared to random initialization, we find that the version +with warm-starting converges dramatically faster, successfully completing pretraining in the same +number of steps as our other M2-BERT encoders. + Finetuning Section 4 describes how GPU memory constraints limit the training batch +size for longer context M2-BERTs, necessitating the use of OPL loss function, which can function +with single-sample batch sizes. We illustrate the batch size-performance tradeoff incurred by the +traditionally used MNRL loss function by comparing (1) OPL trained with batch size 1, to (2) +MNRL trained with the maximum batch size possible on an A100 GPU (Table 8). For fine-tuning +M2-BERT-32k, we find that OPL improved average nDCG@10 on LoCoV1 by 29.4% compared to +MNRL. + +5.3 Applications of M2-BERT Retrieval Encoders +Finally, we explore whether the embeddings from the M2-BERT retrieval model are useful for other +embedding tasks. + Zero-shot Clustering We find our M2-BERT retrieval encoders can be used effectively for +zero-shot clustering of textual datasets. Using our M2-BERT-32k model, we take a sample of the +RedPajama-v1 dataset [6] and generate embeddings for datapoints from each of the constituent +datasets: C4, StackExchange, BookCorpus, ArXiv, and Github. In Figure 3, we visualize the +M2-BERT embeddings for the sampled datapoints from RedPajama. We find that the datapoints +for Github and StackExchange tend to be grouped together, likely due to their overlapping subject +terminologies. Additionally, we find limited overlap between C4 and BookCorpus due to some +shared subjects between the two constituent datasets. Lastly, the ArXiv datapoints seem mostly + + 11 + Loss Batch LoCoV1 ∆ + Model + Function Size Score Scores + M2-BERT-32k MNRL 2 70.4 0 + M2-BERT-32k PL 2 63.2 -7.2 + M2-BERT-32k OPL 1 94.7 24.3 + + Table 8: OPL vs. MNRL for Fine-tuning M2-BERT-32k. + + M2-BERT-32K Embeddings of RedPajama-V1 + + arXiv + GitHub + StackExchange + C4 + Book + + + Figure 3: t-SNE Visualization of M2-BERT-32K Embeddings of RedPajama-V1 sample. + + +isolated by its unique mix of technical topics. + MTEB with M2 To further explore the robustness of the M2-BERT embeddings, we test our +M2-BERT retrieval encoders on the MTEB benchmark tasks. In Table 9, we compare the zero-shot +results of our M2-BERT-128 retrieval encoder to the SentenceBERT baseline for MTEB benchmark, +evaluating on only the English datasets, which cover classification, clustering, pair classification, +reranking, and semantic textual similarity (STS) (for the expanded results, see Appendix A.8). We +found that M2-BERT-128 performed comparably to the SentenceBERT model, scoring 0.2 accuracy +points higher than SentenceBERT, on average, despite substantially less pretraining data and 27% +less parameters. We are interested to explore further applications of M2-BERT in both classification +and clustering tasks, particularly for long-context tasks. + + +6 Conclusion +In this work, we introduce the M2-BERT retrieval encoder, the first retrieval encoder capable +of handling contexts of 32k tokens and the first state-space model retriever. The Monarch Mixer +architecture allows our M2-BERT encoders to scale subquadratically with input context length, +capably handling long-context queries and documents despite only having 80M trainable parameters. +To better understand how M2-BERT and other retrieval encoders can handle long-context queries and +documents, we also developed the LoCoV1 retrieval benchmark, a set of twelve expert-annotated +datasets spanning law, medicine, science, screenwriting, finance, and more. Our M2-BERT retrieval +encoders match Transformer-based retrieval encoders on the BEIR benchmark while achieving +state-of-the-art performance on LoCoV1, beating the next state-of-the-art retrieval encoder by + + + 12 + Model SentenceBERT M2-BERT-128 + Max. Seq. + 512 128 + Length + Classification 64.5 63.4 + Clustering 33.7 32.5 + + + + Tasks + Pair Classification 90.5 90.3 + Reranking 50.9 51.3 + STS 76.1 78.8 + MTEB Avg. Score 63.1 63.3 + ∆ Params 0% -27% + ∆ MTEB Scores 0 +0.2 + + Table 9: M2-BERT vs. SentenceBERT on MTEB. + +19.7 accuracy points while being 3 to 676× more efficient. We are excited to continue exploring +applications of the M2-BERT encoder architecture, such as classification, clustering, and retrieval- +augmented generation (RAG), as well as test other promising fine-tuning approaches, such as cached +MNRL [14, 20]. We hope our M2-BERT retrieval encoders and the LoCoV1 benchmark will bolster +ML pipelines across application domains. + + +7 Acknowledgements +We thank Silas Alberti, Sabri Eyuboglu, Omar Khattab, Gautam Machiraju, Eric Nguyen, Krista +Opsahl-Ong, Christopher Potts, Benjamin Spector, Alyssa Unell, Benjamin Viggiano, Michael +Wornow, and Michael Zhang for their constructive feedback during the composition of the paper. +We would also like to thank our collaborators at the Stanford Artificial Intelligence Laboratory +(SAIL) and TogetherAI. + We gratefully acknowledge the support of NIH under No. U54EB020405 (Mobilize), NSF under +Nos. CCF1763315 (Beyond Sparsity), CCF1563078 (Volume to Velocity), and 1937301 (RTML); +US DEVCOM ARL under No. W911NF-21-2-0251 (Interactive Human-AI Teaming); ONR under +No. N000141712266 (Unifying Weak Supervision); ONR N00014-20-1-2480: Understanding and +Applying Non-Euclidean Geometry in Machine Learning; N000142012275 (NEPTUNE); NXP, +Xilinx, LETI-CEA, Intel, IBM, Microsoft, NEC, Toshiba, TSMC, ARM, Hitachi, BASF, Accenture, +Ericsson, Qualcomm, Analog Devices, Google Cloud, Salesforce, Total, the HAI-GCP Cloud Credits +for Research program, the Stanford Data Science Initiative (SDSI), Stanford EDGE Fellowship, +GEM Fellowship Program, and members of the Stanford DAWN project: Facebook, Google, and +VMWare. Neel Guha is supported by the Stanford Interdisciplinary Graduate Fellowship and the +HAI Graduate Fellowship. The U.S. Government is authorized to reproduce and distribute reprints +for Governmental purposes notwithstanding any copyright notation thereon. 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Retrieve anything to + augment large language models, 2023. + +[54] Yukun Zhu, Ryan Kiros, Rich Zemel, Ruslan Salakhutdinov, Raquel Urtasun, Antonio Torralba, + and Sanja Fidler. Aligning books and movies: Towards story-like visual explanations by + watching movies and reading books. In The IEEE International Conference on Computer + Vision (ICCV), December 2015. + + + + 17 + A Appendix +A.1 LoCoV1 Overview +Table 10 provides an overview of the LoCo benchmark. + + # of Train # of Train # of Test # of Test Avg. Query Avg. Doc. + Dataset Source Domain + Queries Documents Queries Documents Length Length + + SummScreenFD Tau Scrolls Screenwriting 3673 3673 338 338 590 30,792 + + Gov. Report Tau Scrolls Government 17457 17457 972 972 3,871 55,280 + + Corporate + QMSUM Tau Scrolls 1257 1257 272 272 430 58,129 + Management + + QASPER + QASPER Science 888 888 416 416 71 22,315 + Title to Full Text + + QASPER + QASPER Science 888 888 416 416 931 22,315 + Abstract to Full Text + + General + MultiFieldQA LongBench 120 120 30 30 62 29,465 + Domain + + General + 2WikimQA LongBench 240 240 60 60 69 37,867 + Domain + + General + Passage Retrieval LongBench 240 240 60 60 840 35,814 + Domain + + CourtListener - Plain Text CourtListener Law 10000 10000 2000 2000 146 48,190 + + CourtListener - HTML CourtListener Law 10000 10000 2000 2000 146 57,028 + + Australian Legal Australian Legal + Law 3094 3094 770 770 14,986 47,536 + Case Report Case Report + + StackOverflow StackOverflow Programming 1599 18005 400 7741 758 4,544 + + + + Table 10: Overview of Long-Context (LoCo) benchmark (V1) and its constituent datasets. + + + + + 18 + A.2 LoCoV1 Query and Document Examples + +LoCoV1 + Query Example Document Example +Dataset + + It’s the first day of school at Degrassi Community School, + [The Kerwin House - Ashley’s Room] + and eighth-grader Ashley already has her sights set on becoming + the school’s newest student council president. +Tau Scrolls (While getting ready for school, she’s talking to + Her seemingly sure win is soon threatened when her stepbrother, +Summ her friend Terri on the phone.) + Toby, becomes frustrated by her unchallenged status +ScreenFD + and convinces his friend J.T. to run against her. + Ashley: This is gonna be the best year ever! (Working on her + Meanwhile, Emma and Manny deal with eighth-grader + poster for Degrassi... + Spinner’s bullying. Note: This episode marks the first... + + Background + Members of Congress and Administrations have periodically + What is the U.S. International + considered reorganizing the federal government’s trade and + Development Finance Corporation (IDFC)? + development functions to advance various U.S. policy objectives. +Tau Scrolls The IDFC is authorized by statute to be + The Better Utilization of Investments Leading to Development Act +Gov. Report a ”wholly owned Government corporation ... + of 2018 (BUILD Act), which was signed into law on October 5, 2018 + under the foreign policy guidance of the + (P.L. 115-254), represents a potentially major overhaul of U.S. + Secretary of State” in the executive branch. + development finance efforts... + Its purpose is to ”mobilize and... + + Summarize the ideas of the individual presentations. + According to the Industrial Design, there might be only a few choices + Marketing: {vocalsound} That went well , thank you . + for the energy source and materials from the current manufacturer, + Project Manager: That’s great . +Tau Scrolls so he suggested that they had better look for another manufacturer + Industrial Designer: {vocalsound} ’Kay . +QMSUM for more alternatives. The Marketing put forward to design a user + Marketing: Perfect . + friendly interface while the User Interface came up with the idea + Project Manager: Alright , let me just PowerPoint this up . + of including the voice recognition system into the remote control... + {vocalsound} {vocalsound} {vocalsound} Right so um this + + Introduction: Knowledge representation and reasoning +QASPER (KRR) is the process of representing the + Knowledge Authoring and Question Answering with KALM +Title domain knowledge in formal languages + (e.g., SPARQL, Prolog) such that it can be... + + Knowledge representation and reasoning (KRR) is one of the key areas + in artificial intelligence (AI) field. It is intended to represent the world Introduction: Knowledge representation and reasoning +QASPER knowledge in formal languages (e.g., Prolog, SPARQL) and then enhance (KRR) is the process of representing the +Abstract the expert systems to perform querying and inference tasks. Currently, domain knowledge in formal languages + constructing large scale knowledge bases (KBs) with high quality is (e.g., SPARQL, Prolog) such that it can be... + prohibited by the fact that the construction + + \section{Introduction}\label{S1} The multiple access + interferences (MAI) is the root of user limitation in CDMA + systems \cite{R1,R3}. The parallel least mean square-partial +MultiField parallel interference cancelation (PLMS-PPIC) method is a + What algorithm is engaged in the PLMS-PPIC method? +QA multiuser detector for code division multiple access (CDMA) + receivers which reduces the effect of + MAI in bit detection. In this method and + similar to its former version + + Passage 1: The Brave Archer + The Brave Archer, also known as Kungfu Warlord, + is a 1977 Hong Kong film adapted from Louis +2WikimQA Where did the director of film The Brave Bulls (Film) die? + Cha’s novel The Legend of the Condor Heroes. + The film was produced by the Shaw Brothers + Studio and directed by... + + During World War II, navy nurses played a crucial role in + providing medical care and preventing further casualties. They Paragraph 1: Thermometric titrimetry + were present during the initial Japanese attack on Pearl Harbor, Thermometric titrimetry is an +Passage + as well as in Kaneohe Bay, the Philippines, Guam, and extraordinarily versatile technique. +Retrieval + aboard the Solace. The nursing profession was recognized for its This is differentiated from calorimetric + essential contribution and was placed under the War Manpower titrimetry by the fact that the heat... + Commission. Despite shortages, ... + + [c]Sellar v Lasotav Pty Ltd: In the +Court + noting that “[a]s a court of limited jurisdiction, we begin, and end, matter of Lasotav Pty Ltd [2008] FCA 1612 +Listener + with an examination of our jurisdiction” (27 October 2008) +(HTML) + Home Databases WorldLII Search + + [c]Sellar v Lasotav Pty Ltd: In the +Court matter of Lasotav Pty Ltd [2008] + noting that “[a]s a court of limited jurisdiction, we begin, and end, +Listener FCA 1612 (27 October 2008) + with an examination of our jurisdiction” +(Plain Text) Home Databases WorldLII Search + Feedback Federal Court of Australia + + On 14 November 2008, Ms Swee Yen Tay + cited from=”[2006] FCA 1222”& + instituted a proceeding + gt;corporations law +Legal Case in this Court against the Migration Review + cited from=”[2006] FCA 1222”> +Reports Tribunal (”the Tribunal”) + funds held pursuant to terminated deed of company arrangement + and the Minister for Immigration + are held for the benefit of deed creditors... + and Citizenship. + + Multithreading Design Best Practice — Consider this problem: I have + You could use an Observer pattern. A simple functional way +Stack a program which should fetch (let’s say) 100 records from a database, + to accomplish this: “‘ 2k >8k + Model <2k >32k + <8k <32k + BGE-Large + 34.2 39.2 32.6 13.3 + Zero-Shot + Mistral 54.5 60.7 47.9 24.8 + M2-BERT-128 70.8 60.2 34.3 15.4 + M2-BERT-2k 63.9 68.1 47.9 24.1 + M2-BERT-8k 88.5 90.6 89.9 81.1 + M2-BERT-32k 90.4 93.1 94.4 86.1 + +Table 22: M2-BERT Encoder and Baseline Performances by Document Length. Queries are filtered +by whether the token length of their answer documents are in the token range. + + + + + 33 + Model Query Passage + + Running away from home is not a recent phenomenon. Folkloric heroes + This report discusses runaway and homeless + Huckleberry Finn and Davy Crockett fled their abusive fathers to find + youth, and the federal response to support this population. + adventure and employment. Although some youth today also leave home + There is no single definition of the terms ”runaway youth” + BGE due to abuse and neglect, they often endure far more negative outcomes + or ”homeless youth.” However, both groups of youth + Large than their romanticized counterparts from an earlier era. Without + share the risk of not having adequate shelter and + adequate and safe shelter, runaway and homeless youth are vulnerable to + other provisions, and may engage in harmful + engaging in high-risk behaviors and further victimization. Youth who live + behaviors while away from a permanent home. + away from home for extended periods may become removed from... + + Professor B: I think for two years we were two months , uh , away + from being done . + The professor thought it was possible to reduce the + PhD A: And what was that , Morgan ? What project ? + effects of reverberation by removing the low-energy + BGE Professor B: Uh , the , uh , TORRENT chip . + segments. He thought a VAD-like approach would work. + Large PhD A: Oh . + This would make it so that the model was more + Professor B: Yeah . We were two {disfmarker} we were {disfmarker} + likely to keep an echo than throw out speech. + PhD C: Yeah . + Professor B: Uh , uh , we went through it... + + The “officially released” date that appears near the beginning of each + “[i]n deciding cases . . . [j]urors are not expected to opinion is the date the opinion will be published in the Connecticut + lay aside matters of common knowledge or their own Law Journal or the date it was released as a slip opinion. The operative + BGE observations and experiences, but rather, to apply date for the beginning of all time periods for filing postopinion motions + Large them to the facts as presented to arrive at an and petitions for certification is the “officially released” date appearing + intelligent and correct conclusion” (internal in the opinion. All opinions are subject to modification and technical + quotation marks omitted) correction prior to official publication in the Connecticut Reports and + Connecticut Appellate Reports. In the event of discrepancies between... + + In this paper, we describe a new national language technology + programme for Icelandic. The programme, + which spans a period of five years, aims at making + During the last decade, we have witnessed enormous advances in + Icelandic usable in communication and interactions + language technology (LT). Applications that allow users to interact + in the digital world, by developing accessible, open- + with technology via spoken or written natural language are emerging + source language resources and software. The research + in all areas, and access to language resources and open-source software + and development work within the programme is carried + libraries enables faster development for new domains and languages. + out by a consortium of universities, institutions, + E5 However, LT is highly language dependent and it takes considerable + and private companies, with a strong emphasis on + Mistral resources to develop LT for new languages. The recent LT development + cooperation between academia and industries. Five + has focused on languages that have both a large number of speakers and + core projects will be the main content of the + huge amounts of digitized language resources, like English, German, Spanish, + programme: language resources, speech recognition, + Japanese, etc. Other languages, that have few speakers and/or lack digitized + speech synthesis, machine translation, and spell and + language resources, run the risk of being left behind. Icelandic is an example + grammar checking. We also describe other national + of a language with almost a negligible number of speakers, in terms of... + language technology programmes and give an overview + over the history of language technology in + Iceland. + + Brooksley Elizabeth Born (born August 27, 1940) is an American attorney + and former public official who, from August 26, 1996, to June 1, 1999, was + E5 chair of the Commodity Futures Trading Commission (CFTC), the federal agency + Who was Brooksley Elizabeth’s first husband? + Mistral which oversees the U.S. futures and commodity options markets. During her tenure + on the CFTC, Born lobbied Congress and the President to give the CFTC oversight + of off-exchange markets for derivatives, in addition to its role with respect to... + + ACT ONE Scene One - KACL Frasier\’s on + Niles is scanning the society page when he sees a picture of + air at KACL and he\’s running out + Maris with another man. He plans to take an heiress + of time. But Roz still hands him over to his next caller.\nFrasier: Well, + on a date at a society event, the Snow Ball. He then + we\’ve got about thirty seconds. I + realizes that he cannot dance but Daphne then offers + E5 think we\’ve got time for one quick + to teach him. His date cancels, prompting Daphne to + Mistral call. [presses button] Hello, Marlene, + suggest that she go with him to the Ball. At the + I\’m listening.\nMarlene: [v.o.] Oh my + ball, Niles and Daphne dance, to show everyone there + God, I\’m really on?\nFrasier: Yes, your problem, + that he is not mourning his divorce. As they dance a + please...\nMarlene: [dog barking] + tango, Niles declares that he adores Daphne, and... + Lucky, Lucky, get down. George, get the dog... + + Passage 1:\nAlbertine, baroness Staël von Holstein\nHedvig Gustava Albertina, +M2-BERT Which country Albertine, Baroness Staël + Baroness de Staël-Holstein or simply Albertine (1797–1838), was the daughter + 32k Von Holstein’s father is from? + of Erik Magnus Staël von Holstein and Madame de Staël, the granddaughter of... + + The text is about Calvin Zabo, a biochemist who Paragraph 1: With very few feature films made in Canada at all prior + becomes obsessed with the idea of transforming into to the 1960s, in some years no Film of the Year winner was named at all, + a superhuman form similar to the character Mr. Hyde with the awards for Best Short Film or Best Amateur Film instead + in Stevenson’s novel. He robs his employers to fund constituting the highest honour given to a film that year. +M2-BERT his experiments and seeks revenge on Donald Blake, a Even the award for Film of the Year, when presented at all, + 32k doctor who refuses to give him a job. Zabo often also went to a short film. The awards were + successfully creates a formula that transforms him also almost totally dominated by the National Film Board, + into a Hulk-like creature called Mister Hyde. Hyde to the point that independent filmmakers sometimes alleged a systemic + attempts to kill Blake, but Blake transforms into Thor bias which was itself a contributing factor to the difficulty + and survives. of building a sustainable + + J-S79022-17\n\n\nNON-PRECEDENTIAL DECISION - + SEE SUPERIOR COURT I.O.P. 65.37\n\nCOMMONWEALTH OF + PENNSYLVANIA : IN THE SUPERIOR COURT OF\n : PENNSYLVANIA\n +M2-BERT “[T]he rules of criminal procedure + :\n v. VERNELL MORRIS Appellant : No. 3731 EDA 2016\n\n + 32k require the appointment of counsel in PCRA proceedings.” + Appeal from the PCRA Order November 3, 2016\n + In the Court of Common Pleas of Philadelphia County Criminal Division at\n + No(s): CP-51-CR-1113151-1992\n\n\nBEFORE: GANTMAN, P.J.,... + + +Table 23: LoCoV1 Performance Analysis by Model: Passages that aren’t highlighted were +retrieved successfully while passages highlighted in red were not successfully retrieved. Retrieval +success is defined as whether it was retrieved in the first 10 passages. + + 34 + \ No newline at end of file diff --git a/wizard-work/sphinx/archive-longctx-retrieval-2026-10-26/text/2407.19669v2.txt b/wizard-work/sphinx/archive-longctx-retrieval-2026-10-26/text/2407.19669v2.txt new file mode 100644 index 0000000..c3099d0 --- /dev/null +++ b/wizard-work/sphinx/archive-longctx-retrieval-2026-10-26/text/2407.19669v2.txt @@ -0,0 +1,1393 @@ + mGTE: Generalized Long-Context Text Representation and Reranking + Models for Multilingual Text Retrieval + + Xin Zhang1,2 , Yanzhao Zhang1 , Dingkun Long1 , Wen Xie1 , Ziqi Dai1 , + Jialong Tang1 , Huan Lin1 Baosong Yang1 , Pengjun Xie1 , Fei Huang1 , + Meishan Zhang* , Wenjie Li2 , Min Zhang + 1 + Tongyi Lab, Alibaba Group, 2 The Hong Kong Polytechnic University + {linzhang.zx,zhangyanzhao.zyz,dingkun.ldk}@alibaba-inc.com + + + Abstract Random Encoder 2k Text Encoder 8k Text Encoder + + + We present systematic efforts in building long- + 8k Reranker 8k TRM 1k Text Embedder + context multilingual text representation model +arXiv:2407.19669v2 [cs.CL] 14 Oct 2024 + + + + + (TRM) and reranker from scratch for text re- MLM Pre-Training Contrastive Pre-Training Contrastive Fine-Tuning + trieval. We first introduce a text encoder (base + size) enhanced with RoPE and unpadding, pre- Figure 1: Training pipeline. We first build an 8k long- + trained in a native 8192-token context (longer context multilingual encoder. Then based on it, we train + than 512 of previous multilingual encoders). text representation and reranking models for retrieval. + Then we construct a hybrid TRM and a cross- + encoder reranker by contrastive learning. Eval- + uations show that our text encoder outperforms (Gao et al., 2023) systems have led to an unprece- + the same-sized previous state-of-the-art XLM- dented surge in demand for versatile, plug-and- + R. Meanwhile, our TRM and reranker match + the performance of large-sized state-of-the-art + play TRMs and rerankers. These new applications + BGE-M3 models and achieve better results heavily involve processing long and multilingual + on long-context retrieval benchmarks. Further texts, which could not be addressed by conven- + analysis demonstrate that our proposed models tional encoder-based models and urgently require + exhibit higher efficiency during both training upgraded ones. To this end, some resort to enhanc- + and inference. We believe their efficiency and ing existing multilingual encoders, e.g., XLM-R + effectiveness could benefit various researches (Conneau et al., 2020), with extended context win- + and industrial applications.1 + dow up to 8192 (Chen et al., 2024). Others turn + 1 Introduction to use multilingual LLMs which already have the + required capabilities (Zhang et al., 2023a), but their + Text retrieval aims to find relevant passages or docu- models might be computationally expensive for + ments from a large corpus given a query (Manning, self-hosted search services. + 2008). It is often implemented as a multi-stage + In the English community, it has been proven + process, consisting of two main components: a re- + that training long-context encoders from scratch is + triever and a reranker (Gao et al., 2021a; Zhang + promising for text retrieval (Günther et al., 2023; + et al., 2022; Zhao et al., 2024). The retriever iden- + Nussbaum et al., 2024). In this work, we con- + tifies a set of candidate documents that are poten- + tinue this journey, presenting systematic efforts in + tially relevant to the query based on the similarity + building the long-context multilingual text encoder, + between their sparse (lexical term weights) or/and + TRM, and reranker. We suggest a holistic pipeline + dense representations from a text representation + (Figure 1) as well as several techniques in modeling + model (TRM). While the reranker reorders these + and training for multilingual long-context retrieval. + retrieved candidates to refine the results based on + the relevance score generated by a more precise yet Concretely, we first introduce a text encoder en- + computationally demanding model that processes hanced with Rotary Position Embedding (RoPE, + both the query and a candidate document together. Su et al., 2024) and unpadding (Portes et al., 2023), + Recent advances in large language models pre-trained by masked language modeling (MLM) + (LLMs) and retrieval augmented generation (RAG) (Devlin et al., 2019) via a two-stage curriculum + * Corresponding Author + for the native 8,192 tokens context. Based on our + 1 + Models are released at https://hf.co/Alibaba-NLP/ encoder, we propose a hybrid TRM capable of gen- + gte-multilingual-base. erating both elastic dense (Kusupati et al., 2022) + + 1 + ibility with libraries like FlashAttention (Dao, + ( GELU('(!" ) ⊗ '* ) (#$%& Encoder + Layer 2023), we remove the dropout applied to attention + Gated Linear Unit + LayerNorm scores. In addition, we pad the token embedding + size to be a multiple of 64, which could speedup + Gated Linear Unit + Memory Efficient Attention + the model throughput (Portes et al., 2023). + ×N + LayerNorm + Unpadding Mode Inspired by Portes et al. + RoPE + + Q K V Var. Len. Attention + (2023), we unpad the input batch to reduce redun- + w/ dant computations associated with padding tokens + QKV Projection + RoPE + (Figure 2). We use xFormers (Lefaudeux et al., + 2022) to implement the variable length attention. + num_tokens Embedding + A A A A + It dispatch the attention forward and backward to + A A A A B C C + B Unpadding different kernels3 based on the numerical precision, + C C 4 1 2 + + max_seq_len batch_size Unpadding + Tokenization attention head size and device type. We unpad the + MLM labels as well to reduce the computation cost + Figure 2: Our text encoder architecture. of predicting non-masked tokens. + Data We assemble our multilingual pre-training +and sparse vectors for efficient first-stage retrieval, data from a combination of the following sources: +as well as a cross-encoder reranker. We construct C4 (Raffel et al., 2020), Skypile (Wei et al., 2023) +them via the contrastive learning objective (Wang (2021-2023 subsets), mC4 (Xue et al., 2021), Cul- +et al., 2022; Li et al., 2023) with large-scale metic- turaX (Nguyen et al., 2024), Wikipedia (Founda- +ulously curated datasets, providing robust off-the- tion) and books (proprietary). We filter them and +shelf retrieval models. curate a dataset covering 75 Languages. Appendix + We conduct extensive experiments to verify our Table 7 presents the statistics of our dataset. +method. For the text encoder, we evaluate on + Training Curriculum We pre-train the model +two natural language understanding (NLU) bench- + via masked language modeling (MLM) (Devlin +marks, i.e., XTREME-R (Ruder et al., 2021) and + et al., 2019)4 . The MLM probability is set to 30% +GLUE (Wang et al., 2018), and show that our en- + (Portes et al., 2023). Following Conneau and Lam- +coder outperforms the same-sized previous state- + ple (2019) and Conneau et al. (2020), the data from +of-the-art XLM-R. For the TRM and reranker, we + different languages is sampled by a multinomial +evaluate on multiple retrieval benchmarks with + distribution with probabilities {qi }i=1...N , where +multilingual and long-context settings, e.g., MIR- +ACL (Zhang et al., 2023b) and MLDR (Chen et al., pα ni +2024), where our models match the performance qi = PN i with pi = PN , (1) + α + j=1 pj j=1 nj +of state-of-the-art BGE-M3 (Chen et al., 2024) +and achieve better long-context performance by and ni is the number of texts in language i. We +a smaller size. We open-source our models and set α = 0.5. This sampling strategy could increase +code to facilitate further research and applications. texts from low-resource languages. To train the na- + tive 8192-context model more efficiently, we adopt +2 Method a phased training curriculum (Xiong et al., 2024): +2.1 Text Encoder • MLM-2048: we chunk the input into 2048 + tokens and set RoPE base to 10, 000. +To construct powerful long-context multilingual + • MLM-8192: we chunk the input into 8192 +text encoder models, we implement several en- + tokens and set RoPE base to 160, 000. +hancements to BERT (Devlin et al., 2019) architec- + Through this method, we could train the model +ture and train it from scratch using the vocabulary + with a large context length in limited resources 5 . +of XLM-R2 (Conneau et al., 2020) series. + 3 + Specifically, we replace the absolute positional We adopt the memory-efficient attention (Rabe and Staats, + 2021) in this work. +embeddings with RoPE (Su et al., 2024), and up- 4 + We remove the next sentence prediction objective of +grade the feedforward network (FFN) to gated lin- BERT following (Liu et al., 2019). + 5 +ear unit (GLU) (Shazeer, 2020). To ensure compat- In our early experiments of English models, we investi- + gated continue training by RetroMAE (Xiao et al., 2022) after + 2 + https://hf.co/FacebookAI/xlm-roberta-base MLM-8192. However, we did not observe any improvement. + + + 2 + w w w ··· w s + e Matryoshka Embedding model with a batch size of 16, 384 and a learn- + linear linear + Vector Scalar ing rate of 5e − 4 for 240k steps. Each batch is + e ··· sampled from a single data source by the same dis- + tribution of Eq.1. The queries (resp. documents) + Text Representation Model Text Reranker + are truncated to the max tokens of 512 (resp. 1024). + CLS Query / Document CLS Query SEP Document We reverse scale the RoPE base from 160, 000 to + 20, 000 to fit the 1024 context length and acquire + Figure 3: Our TRM and reranker. + the long-context retrieval ability (denotes revNTK, + ablation in §3.4). We set τ of InfoNCE to 0.01 +Training Setup Following Portes et al. (2023), and only use in-batch negatives. More details refer +we use the learning rate decoupled6 AdamW to Appendix B.3. We denote this contrastive pre- +(Loshchilov and Hutter, 2018) with weight decay trained model as mGTE-CPT, which is actually an +1e − 5. We disable gradient clipping (set to 0) (Liu unsupervised embedding model. +et al., 2019). All models are trained on A100 GPU + Matryoshka Embedding Many of recently re- +servers by BF16 PyTorch native automatic mixed + leased models and APIs offer elastic embeddings +precision via transformers (Wolf et al., 2020). + by Matryoshka representation learning (MRL) +We list the detailed hyper-parameters of each train- + (Kusupati et al., 2022), providing competitive sub- +ing stage in Appendix A.2 and Table 8. We denote + vectors of embeddings to save index storage and +the resulting models as mGTE-MLM-2048/8192. + speedup search. Let e ∈ RH denotes an embed- +2.2 Text Representation Model ding and e:d is the sliced sub-vector from dimen- + sion 0 to d < H. MRL7 optimizes the weighted +Based on our encoder, we construct the TRM for + sum of multiple losses from different d dimensional +the first-stage text retrieval in two steps: contrastive + sub-vectors, i.e., compute InfoNCE by sd (eq:d , ed:d ). +pre-training and fine-tuning (Wang et al., 2022; Li + We add this objective to our TRM fine-tuning stage. +et al., 2023). Both steps share the same InfoNCE +(Oord et al., 2018) learning objective: Sparse Representation Chen et al. (2024) show + that neural sparse representations (term/token + exp(s(q, d+ )/τ ) + L = − log PN , (2) weights predicted by TRM) could greatly improve + i + i=1 exp(s(q, d )/τ ) the long-context retrieval performance. We fol- +where τ , q, and d denote the temperature param- low this design, computing the term weight wt of +eter, query and document. The positive d+ is the each token of the input by wt = ReLU(W ht ), +relevant document to q, and other irrelevant docu- where ht is the encoder hidden state of token t +ments are negatives. These negatives can be either with dimension size H and W ∈ RH×1 is ran- +hard-negatives or in-batch negatives (documents of domly initialized. If a token appears multiple +other instances in the same batch). s(q, d) is the times in the text, we keep the max weight. The +relevance score of q and d, measured by the dot relevance score is computed by the joint impor- +product or cosine similarity between their respec- tance of the co-occurring terms (denoted as q ∩ d) +tive representations. within the query and document pair: ssparse (q, d) = + P q d + t∈q∩d (wt · wt ). This is then used to derive the +Contrastive Pre-Training We take the encoder InfoNCE loss for training. +output hidden state of the [CLS] token as the dense +representation (i.e., embedding) and compute the Contrastive Fine-Tuning Now we construct the +relevance score by cosine similarity. Our pre- TRM by multi-task learning of matryoshka embed- +training data (Appendix Table 9) comprise natu- ding and sparse representation: +rally occurring text pairs (e.g., question-answer X +pairs from Quora and StackExchange, title-content LTRM = λLsparse + wd L:d , (3) + d∈D +pairs of CommonCrawl), translation pairs (Team +et al., 2024), and crosslingual instruction tuning where D = {32k | k ∈ N, k ≥ 1, 32k ≤ H} is +data (Muennighoff et al., 2023b). We train the MRL dimension set, wd is the weight of dimension + 6 + However, Xie et al. (2023b) state that the decoupled d, and λ is the weight of sparse representation loss. +weight decay is not ideal. We recommend to keep the de- + 7 +fault setting. Here we mean the MRL-E in Kusupati et al. (2022). + + + 3 + Pair Class. M.C. Structure Prediction Question Answering Cross-lingual Retrieval + Model Avg. + XNLI XCOPA UDPOS WikiANN XQuAD MLQA TyDiQA-GoldP Mewsli-X LAReQA Tatoeba + #Languages (Total 50) 15 11 38 47 11 7 9 38 11 38 + Metrics Acc. Acc. F1 F1 F1/EM F1/EM F1/EM mAP@20 mAP@20 Acc. + mBERT-base 59.43 66.63 55.49 71.80 62.34 66.23 / 51.03 57.37 / 42.44 55.01 / 38.05 44.65 75.26 39.49 + XLM-R-base 62.02 74.50 50.45 73.84 61.23 72.83 / 58.01 61.54 / 46.45 53.09 / 37.11 42.09 63.43 67.20 + mGTE-MLM-2048 65.24 73.17 63.62 73.25 60.87 75.33 / 60.00 64.02 / 48.57 53.58 / 36.68 44.41 72.13 72.02 + mGTE-MLM-8192 64.44 73.37 61.98 73.14 59.83 74.81 / 59.37 64.24 / 48.80 49.85 / 33.27 44.52 71.54 71.10 + + +Table 1: XTREME-R (Ruder et al., 2021) results in the cross-lingual zero-shot transfer (models are trained on +English data) setting. M.C. stands for Multiple Choice. The EM scores are not included in the average. + + + Model Params Pos. Seq. Len. GLUE Avg. Model Seq. en zh fr pl + RoBERTa-baseα 125M Abs. 512 86.4 BGE-M3-unsupervised† 8192 56.48 57.53 57.95 55.98 + XLM-R-base 279M Abs. 512 80.44 512∗ 60.16 58.67 59.72 57.66 + mGTE-MLM-2048 2048 83.42 + mGTE-CPT + 305M RoPE 8192 60.04 58.63 59.74 57.11 + mGTE-MLM-8192 8192 83.47 + mE5-base 514 59.45 56.21 56.19 55.62 + mE5-large 514 61.50 58.81 56.07 60.08 +Table 2: GLUE (Wang et al., 2018) devset averages (w/o + BGE-M3 (Dense)† 8192 59.84 60.80 58.79 60.35 +WNLI). The detailed scores for each subset are shown mGTE-TRM (Dense) 8192 61.40 62.72 59.79 58.22 +in Table 13. α Taken from Table 8 of Liu et al. (2019). + E5-mistral-7b 32768 66.63 60.81 48.33 - +The rest are from our runs, refer to Appendix C.2. + voyage-multilingual-2 32000 - - 61.65 - + Cohere-multilingual-v3.0 512 64.01 - 56.02 - + OpenAI-3-large 8191 64.59 - - - + OpenAI-3-small 8191 62.26 - - - +We fine-tune our contrastive pre-trained embedding +model on diverse high-quality datasets with hard- + Table 3: Embedding model performance on MTEB +negatives (e.g., MS MARCO (Nguyen et al., 2016), English (Muennighoff et al., 2023a), Chinese (Xiao +MIRACL (Zhang et al., 2023b), listed in Table 11). et al., 2024), French (Ciancone et al., 2024) and Polish +We adopt a dynamic batching strategy (Chen et al., (Poświata et al., 2024). The scores of other models are +2024) to fine-tune 8192-context data. The batch retrieved from the MTEB online leaderboard. ∗ To be +sampling strategy is the same as the pre-training consistent with the setting in contrastive pre-training, in +stage. The τ of MRL and sparse is set to 0.05 and retrieval tasks, the max sequence length of the document + side is set to 1024. † Denote our runs. +0.01 respectively. Other details refer to Appendix +B.3. We denote this fine-tuned model as mGTE-TRM. + 3 Evaluation +2.3 Text Reranking Model We separately evaluate our text encoder in §3.1, + TRM and reranker in §3.2 and §3.3. +We also build a reranker using the cross-encoder +architecture. It takes the query q and document d 3.1 Natural Language Understanding +together as input: [CLS] q [SEP] d, and directly We evaluate the encoder on the cross-lingual nat- +predicts their relevance score by the [CLS] output ural language understanding (NLU) benchmark +state: srerank = W h[CLS] . In our experiment, W ∈ XTREME-R9 (Ruder et al., 2021) and the English +RH×1 is randomly initialized. NLU benchmark GLUE (Wang et al., 2018). Re- + The model is fine-tuned by InfoNCE in one step8 sults show that our encoder outperforms the same- +based on our pre-trained 8k-context text encoder sized previous state-of-the-art XLM-R (Conneau +model. Unless otherwise specified, we employ et al., 2020) on all benchmarks. +identical data and training settings as our TRM XTREME-R We focus on the zero-shot cross- +fine-tuning stage (§2.2). The difference lies in our lingual transfer setting where models are fine- +adjustment of the hard-negatives. We describe the tuned on English trainset and tested on multi- and +detailed settings in Appendix B.4. We denote this cross-lingual data. The fine-tuning setup is de- +model as mGTE-reranker. scribed in Appendix C.1. We run mBERT-base, + 9 + We use XTREME-R (Ruder et al., 2021) instead of + 8 + We found that the contrastive pre-training of reranker does XTREME (Hu et al., 2020) since we found the retrieval tasks +not improve the performance. of XTREME is unstable and difficult to evaluate. + + + 4 + Params Seq. Len. Avg. MLDR MIRACL MKQA BEIR LoCo + Metric nDCG@10 nDCG@10 recall@20 nDCG@10 nDCG@10 + #languages (Total 33) 13 18 25 1 1 + BM25 - - 47.0 53.6 31.9 28.1 41.7 79.9 + mE5-base 279M 514 53.5 30.5 62.3 53.7 48.9 72.2 + mE5-large 560M 514 57.7 34.2 65.4 63.5 51.4 74.3 + E5-mistral-7b 7111M 32768 62.4 42.6 62.2 62.4 56.9 87.8 + OpenAI-3-large - 8191 - - 54.9 62.1 55.4 79.4 + BGE-M3 Dense 64.3 52.5 67.7 67.8 48.7 84.9 + BGE-M3 Sparse 568M 8192 55.1 62.2 53.9 36.3 38.3 84.9 + BGE-M3 Dense + Sparse 67.7 64.8 68.9 68.1 49.4 87.4 + mGTE-TRM Dense 66.7 56.6 62.1 65.8 51.1 88.9 + mGTE-TRM Sparse 304M 8192 57.2 71.0 55.9 31.6 39.2 88.1 + mGTE-TRM Dense + Sparse 68.9 71.3 64.5 66.0 51.4 91.3 + + Table 4: Retrieval results on MIRACL (Zhang et al., 2023b) and MLDR (Chen et al., 2024) (multilingual), MKQA + (Longpre et al., 2021) (crosslingual), BEIR (Thakur et al., 2021) and LoCo (Saad-Falcon et al., 2024) (English). +MTEB English average score + + + + + forms BGE-M3-unsupervised (Chen et al., 2024) + 64 + on all four subsets, through our backbone has + fewer params than XLM-R-large. Comparing with + 62 + BGE-M3 and mE5 (Wang et al., 2024b), our final + Our-TRM + nomic-v1.5 TRM achieves best scores on Chinese and French, + 60 + OpenAI-3-small and is competitive on English. + OpenAI-3-large + 128 512 1024 1536 3072 Elastic Embedding We compare our TRM (only + Embedding vector size elastic embeddings) with open-source model and + Figure 4: Elastic embedding results on MTEB English. commercial APIs on MTEB English (Figure 4). + Our model presents close scores to the same-sized + English-only nomic-v1.5, which is promising for + XLM-R-base, and our encoder, as shown in Table a multilingual model. However, it is still behind + 1. Our 2048 and 8192 encoder models achieve av- OpenAI APIs, which is reasonable since they are + erage scores that are higher than those of XLM-R guessed to be much larger models. + by 3.22 and 2.42 points, respectively. + GLUE We also report the performance on the de- 3.3 Text Retrieval + vset of GLUE benchmark (Wang et al., 2018). The + We conduct evaluations to our TRM and reranker + fine-tuning details refer to Appendix C.2. Table + on retrieval benchmarks in multilingual (Miracl + 2 presents the average scores (Table 13 provides + (Zhang et al., 2023b) and MLDR (Chen et al., + the full results). Our models consistently outper- + 2024)), crosslingual (MKQA (Longpre et al., + form XLM-R-base and reasonably lag behind the + 2021)) setting, and the commonly used English + English RoBERTa-base (Liu et al., 2019). + BEIR (Thakur et al., 2021) and LoCo (Saad-Falcon + 3.2 Text Embedding et al., 2024). Our models are close to the state-of- + the-art large models on Miracl, MKQA and BEIR, + Our contrastive pre-training actually yields a text + while achieve better scores on long-context datasets + embedding model. To understand the pre-training + MLDR and LoCo. Details are in Appendix E. + and fine-tuning of TRM, and to compare with other + models, we first run the most popular text embed- + First-Stage Retrieval We compare our TRM + ding benchmark MTEB (Muennighoff et al., 2023a) + to the hybrid model BGE-M3 (Chen et al., 2024), + as well as its Chinese, French and Polish versions. + dense models like mE5 (Wang et al., 2024b) and + Multilingual MTEB The results in Table 3 E5-mistral-7b (Wang et al., 2024a), and BM25. + also present the scores of LLM-based models As shown in Table 4, our TRM consistently outper- + and commercial APIs for reference. For con- forms mE5 and OpenAI APIs, better than BGE-M3 + trastive pre-trained models, our model outper- on MLDR, and close to it on the rest parts. + + 5 + Params Seq. Len. Avg. MLDR MIRACL MKQA BEIR + Metric nDCG@10 nDCG@10 recall@20 nDCG@10 + #languages (Total 33) 13 18 25 1 + Retrieval (mGTE-TRM Dense) 304M 8192 58.9 56.6 62.1 65.8 50.9 + jina-reranker-v2-multilingual 278M 8192 59.4 53.2 65.8 68.8 49.7 + bge-reranker-v2-m3 568M 8192 65.7 66.8 72.6 68.7 54.6 + mGTE-reranker 304M 8192 67.4 78.7 68.5 67.2 55.4 + + Table 5: Results of reranking based on the candidates retrieved by our TRM dense model (refer to Table 4). + + + Model Attn. Unpad. + Encoding Search 40 + + + + + MLDR nDCG@10 Avg. + Time Latency + eager 1800s + BGE-M3 + SDPA-MEA + × + 744s + 20.35ms 30 + eager × 695s revNTK-1024 + SDPA-MEA × 298s 20 revNTK-8192 + mGTE-TRM eager ✓ 675s 15.07ms none-1024 + SDPA-MEA ✓ 279s + none-8192 + MEA ✓ 52s 100k 150k 200k 240k + Contrastive Pre-training Steps +Table 6: Dense retrieval efficiency. Encoding time is +running MLDR-hi corpus (3806 texts with average 4456 Figure 5: MLDR scores in contrastive pre-training. +tokens after truncating to maximum 8192) on one A100 none keeps the RoPE untouched in pre-training. 1024 +GPU with FP16. Search latency is measured on a faiss and 8192 are the max sequence length in evaluations. +index with 8.8M texts. MEA is the memory-efficient revNTK-8912 recovers the 8k context by NTK scaling. +attention in xFormers. SDPA-MEA denotes MEA dis- +patched by scaled dot-product attention of PyTorch. + performance on 1k context but achieve more stable + 8k performance across different training steps. +Reranking In Table 5, we evaluate rerankers +based on the candidates retrieved by Our-TRM +dense model. Our model outperforms the powerful 4 Related Work +bge-reranker-v2-m3 (Chen et al., 2024) with a +smaller size. Moreover, it greatly surpasses the Training long-context TRMs has become a hot +same-sized jina-reranker-v2-multilingual. topic recently. OpenAI released 8191 context APIs + (Neelakantan et al., 2022) have set the target for +3.4 Analysis open-source community. Portes et al. (2023) and + Günther et al. (2023) replace position embedding +Efficiency We compare the efficiency of our + of BERT with Alibi (Press et al., 2022) attention +TRM with BGE-M3 on dense retrieval in Table 6. To + bias and pre-train from scratch, which is shown to +simulate the real-world scenario, the encoding time + be effective in build 8k TRMs. Nussbaum et al. +is the duration of encoding texts without length + (2024) explore the more powerful RoPE (Su et al., +grouping. Our TRM is up to 14 times faster than + 2024) in BERT pre-training and their 2048-context +BGE-M3 (52s v.s. 744s). The end-to-end unpadding + pre-trained encoder achieve better retrieval perfor- +with xFormers is crucial for encoding, which re- + mance on English. Zhu et al. (2024) suggest patch +duces the time by 5 times (52s v.s. 279s). + E5 (Wang et al., 2022) with RoPE. We also use +Scaled Contrastive Pre-Training We utilize the RoPE and provide multi-stage training for native +reversed NTK scaling in contrastive pre-training 8192-context text encoder, TRM, and reranker. +to reduce required text length, where we set the Chen et al. (2024) propose long-context multilin- +RoPE base to 1/8 of the original and train the 8k gual TRM and reranker based on XLM-RoBERTa- +encoder with 1k max length. To evaluate the ef- large (Conneau et al., 2020) by extending position +fectiveness, we run the same training without the embedding to 8192 via continue training. We pre- +reversed NTK, comparing the MLDR scores in Fig- train native 8k multilingual models from scratch +ure 5. With revNTK, models exhibit slightly lower for better long-context performance and efficiency. + + 6 + 5 Conclusion Jonathan H. Clark, Eunsol Choi, Michael Collins, Dan + Garrette, Tom Kwiatkowski, Vitaly Nikolaev, and +We present the holistic practice of building native Jennimaria Palomaki. 2020. TyDi QA: A benchmark +8192-context multilingual retrieval models. We for information-seeking question answering in typo- +first suggest a text encoder with RoPE and un- logically diverse languages. Transactions of the As- + sociation for Computational Linguistics, 8:454–470. +padding, which is pre-trained by a two-stage MLM +curriculum for 8k context. Evaluations on NLU Alexis Conneau, Kartikay Khandelwal, Naman Goyal, +benchmarks show that our encoder outperforms Vishrav Chaudhary, Guillaume Wenzek, Francisco + Guzmán, Edouard Grave, Myle Ott, Luke Zettle- +XLM-RoBERTa in the same size. Based on our + moyer, and Veselin Stoyanov. 2020. Unsupervised +encoder, we construct a hybrid TRM and a cross- cross-lingual representation learning at scale. In Proc. +encoder reranker by contrastive learning. 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Pengjun Xie, Meishan Zhang, and Min Zhang. 2023a. + + + 10 + Language models are universal embedders. arXiv length 2048 with batch size 8192 for roughly 0.6 + preprint arXiv:2310.08232. epoch (250k steps) on sampled data (by XLM sam- +Xinyu Zhang, Xueguang Ma, Peng Shi, and Jimmy Lin. pling Eq.1). In the second stage, we down sample + 2021. Mr. TyDi: A multi-lingual benchmark for texts shorter than 2048 and continue train the model + dense retrieval. In Proceedings of the 1st Workshop for 30k steps with maximum length 8192 and batch + on Multilingual Representation Learning, pages 127– size 2048. The RoPE base is set to 10, 000 and + 137, Punta Cana, Dominican Republic. + 160, 000 for the first and second stage, respectively +Xinyu Zhang, Nandan Thakur, Odunayo Ogundepo, (Xiong et al., 2024; Liu et al., 2024; Men et al., + Ehsan Kamalloo, David Alfonso-Hermelo, Xi- 2024). + aoguang Li, Qun Liu, Mehdi Rezagholizadeh, and + Jimmy Lin. 2023b. MIRACL: A Multilingual The text encoder is initialized in base size + Retrieval Dataset Covering 18 Diverse Languages. (12 layers of hidden state size 768) by PyTorch + Transactions of the Association for Computational default initialization. We train the model by + Linguistics, 11:1114–1131. transformers library (Wolf et al., 2020) in BF16 +Yanzhao Zhang, Dingkun Long, Guangwei Xu, and precision. Following Portes et al. (2023), we use + Pengjun Xie. 2022. Hlatr: enhance multi-stage text the learning rate decoupled AdamW optimizer with + retrieval with hybrid list aware transformer reranking. weight decay 1e-5. The other hyper-parameters are + arXiv preprint arXiv:2205.10569. in Table 8. During training, we split texts that ex- +Wayne Xin Zhao, Jing Liu, Ruiyang Ren, and Ji-Rong ceed the max sequence length into chunks, but we + Wen. 2024. Dense text retrieval based on pretrained do not modify shorter texts. + language models: A survey. ACM Trans. Inf. Syst., The 250k steps of first stage, MLM-2048, took + 42(4). + 10.75 days on 32 A100 80G GPUs. The 30k steps +Dawei Zhu, Liang Wang, Nan Yang, Yifan Song, Wen- of second stage, MLM-8192, took 20.5 hours on + hao Wu, Furu Wei, and Sujian Li. 2024. Longembed: 32 A100 80G GPUs. We acknowledge that this + Extending embedding models for long context re- + trieval. arXiv preprint arXiv:2404.12096. + is not the optimal setting and recommend further + explorations to optimize the pre-training. +Appendix + A.3 Additional Discussion on RoPE +A MLM Pre-Training We chose RoPE (Su et al., 2024) (to replace abso- +In this section, we describe the data and training lute position embedding) due to its advantageous +configurations of the MLM pre-training of our sug- properties. RoPE offers excellent context exten- +gested text encoder. sion capabilities, allowing models to be trained + on shorter context windows and then run infer- +A.1 Data ence on longer ones. Additionally, it implements +Our multilingual pre-training data are composed asymmetric relative distance encoding, meaning +from following sources: D(i, j) ̸= D(j, i), which appears to be particularly + • C4 (Raffel et al., 2020), important for the training of BERT-like encoder- + • Skypile (Wei et al., 2023) (2021-2023 sub- only models that rely on bidirectional attention. + sets), Furthermore, the effectiveness of RoPE has been + • mC4 (Xue et al., 2021) (excluded English), empirically validated by numerous models, such as + • CulturaX (Nguyen et al., 2024), RoFormer (Su et al., 2024) and LLaMA (Touvron + • Wikipedia (Foundation), et al., 2023). + • books (proprietary). + B Contrastive Learning +We filter them and curate a dataset with 1,028B to- +kens (by XLM-R tokenizer), covering 75 languages In this section, we describe the data and training +(Chinese Simplified and Traditional are counted as configurations of the contrastive learning of our +one). Table 7 presents the statistics of our final TRM and reranker. +dataset. + B.1 Pre-Training Data +A.2 Training Details Following previous studies, we create large-scale +We pre-train out text encoder with a two-stage cur- weakly correlated text pairs from diverse sources. +riculum by masked language model (MLM) objec- The data are primarily consisted of four parts: En- +tive. The first stage model is trained on maximum glish pairs (Wang et al., 2022; Li et al., 2023), + + 11 + ISO code Language Tokens (M) Size (GiB) ISO code Language Tokens (M) Size (GiB) + af Afrikaans 1,489.19 5.30 ky Kyrgyz 500.40 3.27 + ar Arabic 14,549.36 79.53 lo Lao 2.43 0.01 + az Azerbaijani 688.72 3.13 lt Lithuanian 1,824.46 6.38 + be Belarusian 1,090.61 6.17 lv Latvian 1,823.43 6.38 + bg Bulgarian 1,454.57 8.94 mk Macedonian 735.46 4.89 + bn Bengali 1,291.58 9.21 ml Malayalam 778.66 7.27 + ca Catalan 1,294.05 4.65 mn Mongolian 958.83 5.91 + ceb Cebuano 633.06 2.02 mr Marathi 861.05 7.48 + cs Czech 1,465.00 5.27 ms Malay 96.37 0.39 + cy Welsh 582.49 1.84 my Burmese 902.46 7.26 + da Danish 1,030.30 4.01 ne Nepali 657.65 6.32 + de German 18,097.31 67.90 nl Dutch 5,137.98 18.65 + el Greek 874.87 5.09 no Norwegian 992.51 3.91 + en English 187,110.31 771.79 pa Punjabi 726.41 4.96 + es Spanish 148,713.06 601.04 pl Polish 2,949.88 10.42 + et Estonian 1,111.31 4.10 pt Portuguese 49,594.59 198.64 + eu Basque 787.46 2.99 qu Quechua 0.07 0.00 + fa Persian 1,203.16 7.22 ro Romanian 2,215.05 7.98 + fi Finnish 949.88 3.73 ru Russian 93,966.28 597.92 + fr French 136,785.00 512.28 si Sinhala 878.65 7.03 + gl Galician 772.47 3.22 sk Slovak 884.38 3.31 + gu Gujarati 973.27 6.95 sl Slovenian 1,100.81 4.05 + he Hebrew 1,842.74 8.36 so Somali 0.82 0.00 + hi Hindi 1,032.67 8.27 sq Albanian 700.78 2.73 + hr Croatian 480.19 1.54 sr Serbian 1,139.38 6.84 + ht Haitian 0.03 0.00 sv Swedish 840.00 3.37 + hu Hungarian 1,341.23 5.10 sw Swahili 31.58 0.13 + hy Armenian 805.98 4.88 ta Tamil 926.84 8.54 + id Indonesian 25,564.33 119.84 te Telugu 857.91 7.01 + is Icelandic 987.89 3.63 th Thai 12,782.08 119.52 + it Italian 11,068.23 40.50 tl Filipino 275.16 1.01 + ja Japanese 135,684.28 601.19 tr Turkish 1,065.05 4.42 + jv Javanese 0.62 0.00 uk Ukrainian 893.70 5.68 + ka Georgian 834.90 7.25 ur Urdu 1,051.83 6.19 + kk Kazakh 1,020.27 6.57 vi Vietnamese 67,850.87 305.51 + km Khmer 746.15 6.54 yo Yoruba 0.04 0.00 + kn Kannada 919.83 7.15 zh-cn Chinese (Simplified) 43,727.30 167.23 + ko Korean 22,865.85 91.78 zh-tw Chinese (Traditional) 73.39 0.26 + +Table 7: MLM pre-training data, where we have a total of 1,028B tokens (by XLM-RoBERTa tokenizer). The raw +texts are stored in 4.47 TiB arrow files. We report the list of 75 languages (Chinese Simplified and Traditional are +counted as one) and include the number of tokens and the size of the data (arrow files, in GiB) for each language. + + + + + 12 + Hyper-param MLM-2048 MLM-8192 B.3 TRM Training Setup + Number of Params 304M + Here we separately describe the training setting of + Number of Layers 12 + Hidden Size 768 the contrastive pre-training and TRM fine-tuning. + FFN Inner Size 3072 + Number of Attention Heads 12 Contrastive Pre-Training In the contrastive pre- + Attention Head Size 64 training, we train a dense representation model + Dropout 0.1 + (embedder) which take the [CLS] hidden state as + Attention Dropout 0 + Learning Rate Decay Linear the embedding of the input. We use the same XLM + Adam ϵ 1e-6 sampling strategy (eq.1) to sample batches from + Adam β1 0.9 each source of Table 9 or cc-news subset of Table + Adam β2 0.98 10, where the texts of one batch only come from + Gradient Clipping 0.0 + Precision PyTorch BF16 AMP + one single source, and the batch size is 16, 384. We + Weight Decay 1e-5 train the model by transformers with deepspeed + Max Length 2048 8192 ZeRO (Rajbhandari et al., 2020) stage 1 in FP16 + Batch Size 8192 2048 precision for roughly 0.4 epoch (240k steps, took + Peak Learning Rate 5e-4 5e-5 + 154 hours on 16 A100 80G GPUs) of our data + Warm-up Ratio 0.06 0.06 + Max Steps 250000 30000 (3.93B pairs on sampled data by Eq.1). We use + RoPE base 10000 160000 the AdamW optimizer with the learning rate 2e-4, + linear decay, and warm-up ratio 0.05. The β1 = + Table 8: MLM pre-training hyper-parameters. 0.9, β2 = 0.999, and ϵ = 1e − 07. We set gradient + clipping to 1.0. +Chinese pairs (Li et al., 2023; Xiao et al., 2024), + TRM Fine-Tuning In the fine-tuning stage, we +multilingual pairs (cc-news10 ), and crosslingual in- + further train our embedding model with high- +struction and translation pairs (Muennighoff et al., + quality datasets as detailed in §B.2. For each query, +2023b; Team et al., 2024). We filter the data by re- + we incorporate one positive passage and 8 hard neg- +moving duplicates and low-quality pairs, resulting + ative passages. To enhance long-context retrieval +in a total of 2,938.8M pairs. Table 9 lists the statis- + capabilities and maximize training efficiency, we +tics of our contrastive pre-training data (cc-news is + adopt a dynamic batch size strategy as previous +separately presented by languages in Table 10). + work (Chen et al., 2024). Firstly, we group the +B.2 Fine-Tuning Data training data according to their lengths for each +We collect publicly available high-quality dataset dataset. Different batch sizes are then used for +as our fine-tune data as detailed in Table 11. For varying lengths during training. Additionally, we +English, we utilize seven datasets: MS MARCO divide the entire batch into multiple sub-batches, +(Nguyen et al., 2016), Natural Questions (NQ) encoding each sub-batch iteratively with gradient +(Kwiatkowski et al., 2019), TriviaQA (Joshi et al., checkpointing (Chen et al., 2016) and then gather +2017), HotpotQA (Yang et al., 2018), SQuAD (Ra- them to get the final batch’s embeddings. We train +jpurkar et al., 2016), FEVER (Thorne et al., 2018), the embedding model with 10 epochs with 8 A100 +AllNLI from SimCSE (Gao et al., 2021b). For Chi- 80G GPUs. All other hyper-parameters remain +nese, we compile six datasets: DuReader (Qiu et al., consistent with those used in the contrastive pre- +2022), mMARCO-zh (Bonifacio et al., 2021), T2- training stage. In Table 12, we list the batch size of +Ranking (Xie et al., 2023a), CmedQAv2 (Zhang different length. +et al., 2018), SimCLUE11 , Multi-CPR (Long et al., +2022). Additionally, we incorporate three mul- B.4 Reranker Training Setup +tilingual datasets: Mr.TyDi (Zhang et al., 2021), We utilize the identical fine-tuning dataset for both +MIRACL (Zhang et al., 2023b), and MLDR (Chen the reranker and the TRM. For each query, we intro- +et al., 2024). We exclusively use the trainset of duce 10 negative samples, comprising 6 hard nega- +each dataset and employ our contrastive pre-trained tives and 4 randomly selected negatives. All train- +model to mine hard negatives. ing parameters expect batch size are kept consistent + 10 + commoncrawl.org/blog/news-dataset-available with those employed for the TRM. The batch sizes + 11 + https://github.com/CLUEbenchmark/SimCLUE are listed in Table 12. + + 13 + Source Language Pairs (M) Size (GiB) Source Language Pairs (M) Size (GiB) + agnews English 1.15 0.30 stackoverflow_title_body English 18.01 20.49 + amazon_qa English 1.10 0.37 wikihow English 0.13 0.03 + amazon_review_title_body English 87.86 43.58 wikipedia English 33.17 19.39 + arxiv_title_abstract English 2.26 2.26 yahoo_body_answer English 0.68 0.44 + baai_mtp_en English 196.60 178.70 yahoo_qa English 1.20 0.55 + beir_dbpedia English 4.64 1.59 yahoo_question_body English 0.66 0.20 + beir_debate English 0.38 0.63 baai_mtp_zh Chinese 100.13 231.42 + beir_pubmed_title_abstract English 0.13 0.19 baidu_baike Chinese 34.21 39.05 + biorxiv_title_abstract English 0.20 0.32 baike_qa_train Chinese 1.43 1.34 + clueweb English 3.94 6.62 commoncrawl_zh Chinese 28.42 92.79 + clueweb_anchor English 4.51 7.69 gpt3_qa_all Chinese 4.97 2.39 + cnn_dailymail English 0.31 1.28 gpt3_summarization Chinese 4.48 1.62 + commoncrawl English 139.94 506.84 medical_quac_wenda_10m Chinese 10.00 4.55 + dpr_reddit English 199.82 125.71 medical_scholar Chinese 8.43 7.81 + gooaq_qa English 3.01 0.97 qcl Chinese 7.40 43.23 + hlp_wikipedia English 19.48 13.55 web_text_zh_train Chinese 4.12 2.07 + medrxiv_title_abstract English 0.20 0.32 wikipedia Chinese 4.45 1.07 + msmarco English 2.89 19.56 wodao Chinese 59.13 190.29 + npr English 0.59 1.03 zh_sft_data_v1 Chinese 0.45 0.43 + reddit_title_body English 124.89 90.36 zh_sft_data_v2 Chinese 2.24 1.37 + s2orc_citation_abstract English 30.58 67.81 zhihu_qa Chinese 53.42 40.99 + s2orc_citation_title English 51.03 10.84 zhihu_title_body Chinese 0.94 0.29 + s2orc_title_abstract English 41.77 30.29 xp3x Crosslingual 351.87 463.85 + stackexchange_qa English 3.00 3.36 translation_eg_NLLB Crosslingual 940.63 323.06 + stackexchange_title_body English 4.74 4.00 + +Table 9: Contrastive pre-training data, where cc-news multilingual data are not included (Table 10). For this Table, +we have a total of 2,595.57M pairs (raw texts stored by 2.55 TiB jsonl files). + + + + + Lang. Pairs (M) Size (GiB) Lang. Pairs (M) Size (GiB) Lang. Pairs (M) Size (GiB) Lang. Pairs (M) Size (GiB) + ar 20.407 32.45 fy 0.044 0.03 lb 0.048 0.05 sk 1.093 1.16 + az 0.401 0.23 gl 0.114 0.20 lt 0.321 0.24 sl 1.046 0.93 + be 0.039 0.06 gu 0.061 0.06 lv 0.438 0.37 sq 0.282 0.51 + bg 3.005 5.03 he 0.397 0.84 mk 0.173 0.44 sr 0.910 1.09 + bn 0.463 0.33 hi 14.253 29.90 ml 0.408 0.48 sv 3.361 2.90 + ca 0.909 1.30 hr 1.268 1.77 mr 0.278 0.35 sw 0.059 0.07 + cs 1.834 2.18 hu 2.668 3.40 my 0.045 0.04 ta 2.125 1.26 + da 1.090 1.58 hy 0.125 0.09 nl 6.700 7.41 te 0.355 0.33 + de 39.715 57.98 id 6.048 7.46 nn 0.162 0.12 tg 0.038 0.03 + el 7.170 14.93 is 0.100 0.05 no 1.978 2.21 th 0.124 0.17 + en 0.615 1.47 it 27.827 40.57 or 0.038 0.03 tl 0.055 0.07 + es 55.201 86.87 ja 4.139 3.95 pa 0.036 0.04 tr 23.840 26.81 + et 0.950 0.85 ka 0.074 0.06 pl 3.530 5.77 uk 5.021 8.42 + eu 0.051 0.02 kn 0.192 0.16 pt 12.611 19.28 ur 1.625 0.87 + fa 4.839 7.99 ko 8.605 12.48 ro 6.678 9.15 vi 4.375 7.03 + fi 1.532 1.93 ky 0.061 0.03 ru 39.451 65.74 MIX∗ 0.359 0.28 + fr 21.242 32.67 la 0.035 0.06 sh 0.220 0.18 + +Table 10: The cc-news multilingual pairs (343.26M in total, raw texts stored by 512.8 GiB jsonl files), used in +contrastive pre-training together with all data of Table 9. MIX∗ denotes the mixed pairs of languages that are +less than 1GiB (such as af, ceb). We utilize a very large batch size (16, 384), and since each batch contains text +exclusively from a single source, these low-resource languages might not fill an entire batch. Consequently, we have +merged these languages together. + + + + + 14 + Dataset Language Size XNLI We fine-tune the model on MNLI16 + MS MARCO, HotpotQA, NQ, NLI, etc. English 1.4M (Williams et al., 2018) trainset and then evaluate + DuReader, T2 -Ranking, SimCLUE, etc. Chinese 2.0M the checkpoint on XNLI17 (Conneau et al., 2018). + MIRACL, Mr.TyDi, MLDR Multilingual 118.9K + XCOPA We run this data as the multiple choice +Table 11: Specification of training data adopted in Fine- task. The model is first trained on SIQA18 citesap- +tuning stage. etal-2019-social and then COPA19 (Roemmele + et al., 2011) for 5 epochs on each dataset. The + length BS(E) S-BS(R) BS(E) S-BS(R) checkpoint of COPA is evaluated on XCOPA20 + 0-500 768 256 512 256 (Ponti et al., 2020). + + 500-1000 384 128 384 128 UDPOS We extract pos-tagging data from the +1000-2000 256 64 256 64 UD (de Marneffe et al., 2021) v2.7 and train the + model on trainset of English parts by 10 epochs. +2000-3000 160 48 160 48 +3000-8000 80 16 80 16 WikiANN We fine-tune the model on the trainset + of English by 10 epochs and evaluate on selected +Table 12: Batch size (BS) and sub batch size (S-BS) WikiANN (Rahimi et al., 2019) testsets21 . +of different length for embedding (E) and reranker (R) +model in the fine-tune stage. XQuAD We fine-tune on the trainset of SQuAD + (Rajpurkar et al., 2016) v1.122 for 3 epochs with + the learning rate 3e-5 and max length 384. Then +C NLU Evaluation we evaluate the checkpoint on XQuAD23 (Artetxe +We evaluate our text encoder as well as baselines on et al., 2020). +the multilingual XTREME-R (Ruder et al., 2021) + MLQA We directly evaluate the same checkpoint +and English GLUE (Wang et al., 2018) benchmarks. + of XQuAD on MLQA24 (Lewis et al., 2020) with +We describe the fine-tuning setup and the evalua- + the same setting. +tion details in the following subsections. The eval- +uation scripts are available in our github repo12 . TyDiQA-GoldP We train the model on TyDiQA- + GoldP25 (Clark et al., 2020) trainset in the same +C.1 XTREME-R + setting as XQuAD. Then we evaluate the check- +We only run XTREME-R (Ruder et al., 2021) in point on the testset. +the zero-shot cross-lingual transfer learning setting, +where models are fine-tuned on English trainset Mewsli-X We generate the data following their +and tested on multi- and cross-lingual data. We github26 . This is a updated version so that we can +compare our encoder with mBERT-base-cased13 not compare with the results in the XTREME-R +and XLM-RoBERTa-base14 . All models are fine- paper. We train the model on the English wikipedia +tuned in the same setting and hyper-parameters. (mention, entity)-pairs for 2 epochs with the batch + The results are already presented in Table 1. size 64 and max length 64. Then we evaluate the + As XTREME-R has no final release, we im- checkpoint in the language agnostic retrieval set- +plement the evaluation code based on the code ting, refer to Ruder et al. (2021) for more details. +of XTREME15 . However, there are some differ- 16 + hf.co/datasets/nyu-mll/glue MNLI subset. +ences in the retrieval evaluation, where our code 17 + hf.co/datasets/facebook/xnli +will deduplicate the retrieval corpus. In addi- 18 + hf.co/datasets/allenai/social_i_qa +tion, we implement the XCOPA in multiple choice, 19 + hf.co/datasets/aps/super_glue copa split. + 20 +which might be different from XTREME-R. In fine- hf.co/datasets/cambridgeltl/xcopa + 21 + hf.co/datasets/unimelb-nlp/wikiann +tuning, if not specified, we use the epoch number 22 + hf.co/datasets/rajpurkar/squad +of 3, learning rate of 2e-5, batch size of 32, and 23 + hf.co/datasets/google/xquad +max sequence length of 128 (Hu et al., 2020). 24 + hf.co/datasets/facebook/mlqa + 25 + 12 + hf.co/datasets/juletxara/tydiqa_xtreme + github.com/izhx/nlu-evals 26 + https://github.com/google-research/ + 13 + hf.co/google-bert/bert-base-multilingual-cased google-research/blob/master/dense_ + 14 + hf.co/FacebookAI/xlm-roberta-base representations_for_entity_retrieval/mel/ + 15 + github.com/google-research/xtreme mewsli-x.md#getting-started + + + 15 + LAReQA This task is actually conducted on MTEB-en Table 14 shows the results on English +XQuAD-R27 (Roy et al., 2020). We fine-tune MTEB (Muennighoff et al., 2023a). For refer- +the model on the trainset of SQuAD v1.1 in dual- ence, we include our English embedding models +encoder architecture ([CLS] as the embedding) and (Our-en-base/large-embed, trained by the two- +retrieval setting for 3 epochs with the batch size stage contrastive learning on the English part of +16, max query length 96, and max document length our data) and top-performing systems from the on- +256. Then we evaluate the checkpoint on XQuAD- line leaderboard. We can see that the multilingual +R in same setting. models still have a noticeable gap compared to the + English models. +Tatoeba We directly evaluate the checkpoint +from LAReQA on Tatoeba28 (Facebook, 2019) in MTEB-zh Table 15 presents the C-MTEB (Xiao +the same setting. et al., 2024) (MTEB Chinese subset) results. We in- + clude the results of several LLM-based embedding +C.2 GLUE models and APIs. Given that the Chinese commu- + nity is also keen on optimizing embedding models, +The GLUE benchmark (Wang et al., 2018) is En- + the gap between multilingual models and Chinese +glish transfer learning, i.e., models are trained and + models is quite noticeable. +tested on the trainset and testset of each dataset +(CoLA (Warstadt et al., 2019), SST-2 (Socher et al., MTEB-fr Table 16 demonstrates the F-MTEB +2013), MRPC (Dolan and Brockett, 2005), STS-B (Ciancone et al., 2024) (MTEB French subset) re- +(Cer et al., 2017), QQP, MNLI (Williams et al., sults. Our TRM dense is comparable to the spe- +2018), QNLI (Rajpurkar et al., 2016), RTE). cialized French API mistral-embed. However, + We evaluate the GLUE benchmark based on the compared to our our-cpt model, the improvement +scripts29 and data30 provided by transformers. from fine-tuning is not significant. +In fine-tuning of each dataset, we use the epoch + MTEB-pl Table 17 lists the Polish MTEB +number of 3, learning rate of 2e-5, batch size of 32, + (Poświata et al., 2024) results. Our model does not +and max sequence length of 128. For MRPC, STS- + outperform large-sized BGE and mE5. We specu- +B, and RTE, we start from the checkpoint of MNLI + late this may be due to the limited amount of Polish +following (Liu et al., 2019). The MNLI checkpoint + pairs in the contrastive pre-training, resulting in +is shared with XNLI of XTREME-R (§C.1). + insufficient training. + The detailed results are in Table 13. We also +include scores of our English models (Our-en-*, E Text Retrieval Evaluation +pre-trained on C4-en) and baselines (Portes et al., +2023; Günther et al., 2023; Nussbaum et al., 2024). The retrieval process can be divided into two main + stages: recall and reranking. In the recall stage, +D Text Embedding Evaluation documents are retrieved using both dense vectors + and sparse representations. The final recall score +We have demonstrated the average scores on is calculated by weighting the dense retrieval score +MTEB English, Chinese, French and Polish (Table with a fixed coefficient of 1 and the sparse retrieval +3). In this section, we delve into the details, pre- score with coefficients ranging from 0.001 to 0.01. +senting results of different tasks on each language. Documents not retrieved by either method receive +For a fair comparison, we do not include the de- a score of 0. During the ranking stage, the top 100 +rived models (developed by secondary training on documents from the recall results are selected as +other public off-the-shelf models) in English and candidates. These candidates are then sorted using +Chinese. In addition to the results obtained from our reranker model to produce the final retrieval +the online leaderboard, our own MTEB evaluations results. +were conducted using version 1.2.0 of mteb library. We present the detail results of MLDR (Chen + 27 et al., 2024) (multilingual long-context retrieval, + hf.co/datasets/google-research-datasets/ +xquad_r Table 18), MKQA (Longpre et al., 2021) (multi- + 28 + hf.co/datasets/mteb/tatoeba-bitext-mining lingual, Table 19), MIRACL (Zhang et al., 2023b) + 29 + github.com/huggingface/transformers/tree/ (multilingual, Table 20, BEIR (Thakur et al., 2021) +main/examples/pytorch/text-classification# +glue-tasks (English, Table 21) and LoCo (Saad-Falcon et al., + 30 + hf.co/datasets/nyu-mll/glue 2024) (English long-context, Table 22). + + 16 + Single Sentence Paraphrase and Similarity Natural Language Inference + Model Params Pos. Seq. Avg. CoLA SST-2 MRPC STS-B QQP MNLI QNLI RTE + RoBERTa-baseα 125M Abs. 512 86.4 63.6 94.8 90.2 91.2 91.9 87.6 92.8 78.7 + MosaicBERT-base-128β 137M Alibi 128 85.4 58.2 93.5 89.0 90.3 92.0 85.6 91.4 83.0 + MosaicBERT-base-2048γ 137M Alibi 2048 85 54 93 87 90 92 86 92 82 + JinaBERT-baseδ 137M Alibi 512 82.6 51.4 94.5 88.4 89.5 80.7 85.7 92.2 78.7 + nomic-bert-2048γ 137M RoPE 2048 84 50 93 88 90 92 86 92 82 + GTEv1.5-en-base-2048 137M RoPE 2048 85.15 54.46 93.81 93.21 90.00 88.61 86.73 91.67 82.67 + GTEv1.5-en-base-8192 137M RoPE 8192 85.61 57.02 93.35 92.14 90.21 88.78 86.69 91.85 84.84 + XLM-R-base 279M Abs. 512 80.44 30.74 92.43 92.74 89.16 87.74 84.54 90.37 75.81 + mGTE-MLM-2048 305M RoPE 2048 83.42 49.65 92.66 91.17 89.95 88.41 85.40 91.38 78.70 + mGTE-MLM-8192 305M RoPE 8192 83.47 48.41 92.32 90.94 89.77 88.50 85.58 91.34 80.87 + RoBERTa-largeα 355M Abs. 512 88.9 68.0 96.4 90.9 92.4 92.2 90.2 94.7 86.6 + MosaicBERT-large-128β 434M Alibi 128 86.1 59.7 93.7 88.2 90.9 92.0 86.9 93.0 84.5 + JinaBERT-largeδ 435M Alibi 512 83.7 59.6 95.0 88.5 88.2 80.9 86.6 92.5 78.5 + GTEv1.5-en-large-512 434M RoPE 512 88.16 64.80 94.50 92.09 91.50 89.23 89.12 93.78 90.25 + GTEv1.5-en-large-2048 434M RoPE 2048 87.02 60.09 94.61 92.14 91.47 89.12 89.02 92.31 87.36 + GTEv1.5-en-large-8192 434M RoPE 8192 87.58 60.39 95.07 93.45 91.37 89.19 89.20 93.90 88.09 + + +Table 13: GLUE (Wang et al., 2018) devset scores (w/o WNLI). α Taken from Table 8 of Liu et al. (2019). β Taken +from Table S3 of Portes et al. (2023). γ Taken from Table 2 of Nussbaum et al. (2024). δ Taken from Table 2 of +Günther et al. (2023). The rest of the numbers are from our runs, refer to §C.2 for details. + + + + + MTEB English Param. Dim. Seq. Avg. Class. Clust. PairC. Rerank. Retr. STS Summ. + #Datasets (→) 56 12 11 3 4 15 10 1 + gte-Qwen2-7b-instruct (Li et al., 2023) 7613M 3584 131072 70.24 86.58 56.92 85.79 61.42 60.25 83.04 31.35 + neural-embedding-v1 - - - 69.94 87.91 54.32 87.68 61.49 58.12 85.24 30.87 + NV-Embed-v1 (Lee et al., 2024a) 7851M 4096 32768 69.32 87.35 52.8 86.91 60.54 59.36 82.84 31.2 + voyage-large-2-instruct - 1024 16000 68.28 81.49 53.35 89.24 60.09 58.28 84.58 30.84 + gte-Qwen2-1.5B-instruct (Li et al., 2023) 1776M 1536 131072 67.16 82.47 48.75 87.51 59.98 58.29 82.73 31.17 + google-gecko (Lee et al., 2024b) 1200M 768 2048 66.31 81.17 47.48 87.61 58.9 55.7 85.07 32.63 + GritLM-7B (Muennighoff et al., 2024) 7242M 4096 32768 66.76 79.46 50.61 87.16 60.49 57.41 83.35 30.37 + E5-mistral-7b (Wang et al., 2024a) 7111M 4096 32768 66.63 78.47 50.26 88.34 60.21 56.89 84.63 31.4 + text-embedding-3-large - 3072 8191 64.59 75.45 49.01 85.72 59.16 55.44 81.73 29.92 + mxbai-embed-large-v1 (Lee et al., 2024c) 335M 1024 512 64.68 75.64 46.71 87.2 60.11 54.39 85 32.71 + nomic-embed-text-v1 (Nussbaum et al., 2024) 137M 768 8192 62.39 74.12 43.91 85.15 55.69 52.81 82.06 30.08 + gte-en-large-v1.5 434M 1024 8192 65.39 77.75 47.96 84.53 58.5 57.91 81.43 30.91 + gte-en-base-v1.5 137M 768 8192 64.11 77.17 46.82 85.33 57.66 54.09 81.97 31.17 + mE5-base (Wang et al., 2024b) 278M 768 514 59.45 73.02 37.89 83.57 54.84 48.88 80.26 30.11 + mE5-large (Wang et al., 2024b) 560M 1024 514 61.5 74.81 41.06 84.75 55.86 51.43 81.56 29.69 + BGE-m3 (dense)† (Chen et al., 2024) 568M 1024 8192 59.84 74.08 37.27 84.50 55.28 48.82 81.37 31.55 + mGTE-TRM (dense) 305M 768 8192 61.40 70.89 44.31 84.23 57.47 51.08 82.11 30.58 + BGE-m3-unsupervised† (Chen et al., 2024) 560M 1024 8192 56.48 69.28 38.52 80.92 54.03 42.26 78.30 32.11 + 512∗ 60.16 72.89 45.05 84.60 58.41 44.93 80.77 29.94 + mGTE-CPT 305M 768 + 8192 60.04 72.70 45.35 84.63 58.36 44.46 80.59 30.77 + + +Table 14: Results on MTEB English subset (Muennighoff et al., 2023a). We compare models from the online +leaderboard, where derived models (developed by secondary training on other public off-the-shelf models) are not +listed. † Denote our runs. ∗ To be consistent with the setting in contrastive pre-training, in retrieval tasks, the max +sequence length of the document side is set to 1024. + + + + + 17 + C-MTEB Param. Dim. Seq. Avg. Class. Clust. PairC. Rerank. Retr. STS + #Datasets (→) 35 9 4 2 4 8 8 + gte-Qwen2-7b-instruct (Li et al., 2023) 7613M 3584 131072 72.05 75.09 66.06 8 7.48 68.92 76.03 65.33 + piccolo-large-zh-v2 (Huang et al., 2024) - - - 70.95 74.59 62.17 90.24 70 74.36 63.5 + OpenSearch-text-hybrid - 1792 512 68.71 71.74 53.75 88.1 68.27 74.41 62.46 + Baichuan-text-embedding - 1024 512 68.34 72.84 56.88 82.32 69.67 73.12 60.07 + gte-Qwen2-1.5B-instruct (Li et al., 2023) 1776M 1536 131072 67.65 71.12 54.61 86.91 68.21 71.86 60.96 + E5-mistral-7b (Wang et al., 2024a) 7111M 4096 32768 60.81 70.17 52.3 72.19 61.86 61.75 50.22 + mE5-base (Wang et al., 2024b) 278M 768 514 56.21 65.35 40.68 67.07 54.35 61.63 46.49 + mE5-large (Wang et al., 2024b) 560M 1024 514 58.81 67.34 48.23 69.89 56 63.66 48.29 + BGE-m3 (dense)† (Chen et al., 2024) 568M 1024 8192 60.80 66.95 45.75 73.98 62.88 65.43 52.43 + mGTE-TRM (dense) 305M 768 8192 62.72 64.27 47.48 78.34 68.17 71.95 52.73 + BGE-m3-unsupervised† (Chen et al., 2024) 560M 1024 8192 57.53 65.04 47.10 64.09 58.14 61.45 48.42 + 512∗ 58.67 64.64 50.21 63.95 63.77 64.23 46.74 + mGTE-CPT 305M 768 + 8192 58.63 64.38 49.84 63.99 64.13 64.30 46.77 + +Table 15: Results on C-MTEB (Xiao et al., 2024) (MTEB Chinese). We compare models from the online leaderboard, +where derived models (developed by secondary training on other public off-the-shelf models) are not listed. † Denote +our runs. ∗ To be consistent with the setting in contrastive pre-training, in retrieval tasks, the max sequence length of +the document side is set to 1024. + + + F-MTEB Param. Dim. Seq. Avg. Class. Clust. PairC. Rerank. Retr. STS Summ. + #Datasets (→) 26 6 7 2 2 5 3 1 + gte-Qwen2-7b-instruct (Li et al., 2023) 7613M 3584 131072 68.25 81.76 55.56 90.43 78.7 55.65 82.31 31.45 + gte-Qwen2-1.5B-instruct (Li et al., 2023) 1776M 1536 131072 66.6 78.02 55.01 86.88 83.76 52.56 81.26 30.5 + voyage-multilingual-2 - 1024 32000 61.65 68.56 46.57 78.66 82.59 54.56 80.13 29.96 + voyage-law-2 - 1024 16000 60.58 68.45 44.23 77.3 82.06 52.98 80.29 30.34 + mistral-embed - 1024 - 59.41 68.61 44.74 77.32 80.46 46.81 79.56 31.47 + E5-mistral-7b (Wang et al., 2024a) 7111M 4096 32768 48.33 57.72 41.16 76.08 62.2 23.44 65.36 32.22 + mE5-base (Wang et al., 2024b) 278M 768 514 56.19 66.8 42.66 74.82 71.76 41.19 77.22 30.76 + mE5-large (Wang et al., 2024b) 560M 1024 514 56.07 68.39 38.7 76.19 72.14 42.17 79.37 30.92 + BGE-m3 (dense)† (Chen et al., 2024) 568M 1024 8192 58.79 71.57 36.54 79.78 77.36 51.13 80.78 31.05 + mGTE-TRM (dense) 305M 768 8192 59.79 68.72 41.66 79.47 76.47 52.97 81.36 29.74 + BGE-m3-unsupervised† (Chen et al., 2024) 560M 1024 8192 57.95 69.87 38.43 78.51 75.42 50.05 77.18 28.80 + 512∗ 59.72 70.79 41.15 80.29 76.19 53.44 76.87 29.04 + mGTE-CPT 305M 768 + 8192 59.74 70.69 41.07 79.56 77.10 53.55 77.24 28.74 + + +Table 16: Results on F-MTEB (Ciancone et al., 2024) (MTEB French). We compare top-performing models from +the online leaderboard. † Denote our runs. ∗ To be consistent with the setting in contrastive pre-training, in retrieval +tasks, the max sequence length of the document side is set to 1024. + + + MTEB Polish Param. Dim. Seq. Avg. Class. Clust. PairClass. Retr. STS + #Datasets (→) 26 7 1 4 11 3 + gte-Qwen2-7b-instruct (Li et al., 2023) 7613M 3584 131072 67.86 77.84 51.36 88.48 54.69 70.86 + gte-Qwen2-1.5B-instruct (Li et al., 2023) 1776M 1536 131072 64.04 72.29 44.59 84.87 51.88 68.12 + mmlw-roberta-large (Dadas et al., 2024) 435M 1024 514 63.23 66.39 31.16 89.13 52.71 70.59 + mmlw-e5-large (Dadas et al., 2024) 560M 1024 514 61.17 61.07 30.62 85.9 52.63 69.98 + mmlw-roberta-base (Dadas et al., 2024) 124M 768 514 61.05 62.92 33.08 88.14 49.92 70.7 + mE5-base (Wang et al., 2024b) 278M 768 514 55.62 59.01 24.97 82.15 44.01 65.13 + mE5-large (Wang et al., 2024b) 560M 1024 514 60.08 63.82 33.88 85.5 48.98 66.91 + BGE-m3 (dense)† (Chen et al., 2024) 568M 1024 8192 60.35 65.15 25.21 86.46 48.51 69.44 + mGTE-TRM (dense) 305M 768 8192 58.22 60.15 33.67 85.45 46.40 68.92 + BGE-m3-unsupervised† (Chen et al., 2024) 560M 1024 8192 55.98 60.30 40.17 79.01 43.26 67.05 + 512∗ 57.66 62.72 38.04 79.70 45.55 67.39 + mGTE-CPT 305M 768 + 8192 57.11 61.55 38.15 79.53 45.29 66.53 + +Table 17: Results on MTEB Polish subset (Poświata et al., 2024) We compare top-performing models from the +online leaderboard. † Denote our runs. ∗ To be consistent with the setting in contrastive pre-training, in retrieval +tasks, the max sequence length of the document side is set to 1024. + + + + 18 + Max Length Avg ar de en es fr hi it ja ko pt ru th zh + BM25 8192 53.6 45.1 52.6 57.0 78.0 75.7 43.7 70.9 36.2 25.7 82.6 61.3 33.6 34.6 + mE5large 512 34.2 33.0 26.9 33.0 51.1 49.5 21.0 43.1 29.9 27.1 58.7 42.4 15.9 13.2 + mE5base 512 30.5 29.6 26.3 29.2 45.2 46.7 19.0 40.9 24.9 20.9 50.8 37.8 12.2 12.8 + E5mistral-7b 8192 42.6 29.6 40.6 43.3 70.2 60.5 23.2 55.3 41.6 32.7 69.5 52.4 18.2 16.8 + BGE-m3-Dense 8192 52.5 47.6 46.1 48.9 74.8 73.8 40.7 62.7 50.9 42.9 74.4 59.5 33.6 26.0 + BGE-m3-Sparse 8192 62.2 58.7 53.0 62.1 87.4 82.7 49.6 74.7 53.9 47.9 85.2 72.9 40.3 40.5 + BGE-m3-Dense+Sparse 8192 64.8 63.0 56.4 64.2 88.7 84.2 52.3 75.8 58.5 53.1 86.0 75.6 42.9 42.0 + mGTE-TRM Dense 8192 56.6 55.0 54.9 51.0 81.2 76.2 45.2 66.7 52.1 46.7 79.1 64.2 35.3 27.4 + mGTE-TRM Sparse 8192 71.0 74.3 66.2 66.4 93.6 88.4 61.0 82.2 66.2 64.2 89.9 82.0 47.4 41.8 + mGTE-TRM Dense+Sparse 8192 71.3 74.6 66.6 66.5 93.6 88.6 61.6 83.0 66.7 64.6 89.8 82.1 47.7 41.4 + + mGTE-reranker 8192 73.8 76.6 70.4 69.3 96.4 89.6 67.8 81.9 68.1 71.1 90.2 86.1 46.7 44.8 + +Table 18: Evaluation of multilingual long-doc retrieval on the MLDR (Chen et al., 2024) testset (measured by +nDCG@10). + + Baselines M3-Embedding mGTE-TRM mGTE-reranker + BM25 mDPR mContriever mE5large mE5base E5mistral-7b OpenAI-3 Dense Sparse Multi-vec D+S All Dense Sparse D+S ReRank +ar 13.4 33.8 43.8 59.7 44.3 47.6 55.1 61.9 19.5 62.6 61.9 63.0 55.9 17.5 56.0 58.2 +da 36.2 55.7 63.3 71.7 63.6 72.3 67.6 71.2 45.1 71.7 71.3 72.0 69.8 37.9 69.7 71.0 +de 23.3 53.2 60.2 71.2 62.3 70.8 67.6 69.8 33.2 69.6 70.2 70.4 68.9 27.0 69.1 70.1 +es 29.8 55.4 62.3 70.8 63.8 71.6 68.0 69.8 40.3 70.3 70.2 70.7 69.6 35.1 70.0 71.0 +fi 33.2 42.8 58.7 67.7 53.0 63.6 65.5 67.8 41.2 68.3 68.4 68.9 64.2 35.3 64.5 64.9 +fr 30.3 56.5 62.6 69.5 61.2 72.7 68.2 69.6 43.2 70.1 70.1 70.8 69.8 36.9 70.4 71.0 +he 16.1 34.0 50.5 61.4 37.4 32.4 46.3 63.4 24.5 64.4 63.5 64.6 55.4 22.0 55.4 56.5 +hu 26.1 46.1 57.1 68.0 55.9 68.3 64.0 67.1 34.5 67.3 67.7 67.9 64.6 28.8 65.0 66.1 +it 31.5 53.8 62.0 71.2 61.6 71.3 67.6 69.7 41.5 69.9 69.9 70.3 69.0 36.2 69.2 70.1 +ja 14.5 46.3 50.7 63.1 51.7 57.6 64.2 67.0 23.3 67.8 67.1 67.9 65.3 19.5 65.2 67.2 +km 20.7 20.6 18.7 18.3 28.2 23.3 25.7 58.5 24.4 59.2 58.9 59.5 53.6 21.9 53.8 54.7 +ko 18.3 36.8 44.9 58.9 40.4 49.4 53.9 61.9 24.3 63.2 62.1 63.3 55.9 21.4 56.1 58.9 +ms 42.3 53.8 63.7 70.2 62.4 71.1 66.1 71.6 52.5 72.1 71.8 72.3 69.9 47.8 70.2 70.9 +nl 42.5 56.9 63.9 73.0 65.0 74.5 68.8 71.3 52.9 71.8 71.7 72.3 70.7 47.4 70.9 71.5 +no 38.5 55.2 63.0 71.1 62.0 70.8 67.0 70.7 47.0 71.4 71.1 71.6 69.1 39.7 69.2 70.2 +pl 28.7 50.4 60.9 70.5 57.2 71.5 66.1 69.4 36.4 70.0 69.9 70.4 68.4 31.4 68.3 69.6 +pt 31.8 52.5 61.0 66.8 58.7 71.6 67.7 69.3 40.2 70.0 69.8 70.6 69.6 34.9 69.6 70.7 +ru 21.8 49.8 57.9 70.6 58.7 68.7 65.1 69.4 29.2 70.0 69.4 70.0 68.5 25.8 68.5 69.6 +sv 41.1 54.9 62.7 72.0 61.3 73.3 67.8 70.5 49.8 71.3 71.5 71.5 69.5 43.3 69.9 70.6 +th 28.4 40.9 54.4 69.7 59.7 57.1 55.2 69.6 34.7 70.5 69.8 70.8 65.0 30.6 65.2 66.9 +tr 33.5 45.5 59.9 67.3 59.2 65.5 64.9 68.2 40.9 69.0 69.1 69.6 67.7 36.0 67.7 69.0 +vi 33.6 51.3 59.9 68.7 60.0 62.3 63.5 69.6 42.2 70.5 70.2 70.9 69.4 37.6 69.3 70.3 +zh_cn 19.4 50.1 55.9 44.3 38.3 61.2 62.7 66.4 26.9 66.7 66.6 67.3 68.2 23.2 68.4 69.5 +zh_hk 23.9 50.2 55.5 46.4 38.3 55.9 61.4 65.8 31.2 66.4 65.9 66.7 63.7 27.8 63.8 65.8 +zh_tw 22.5 50.6 55.2 45.9 39.0 56.5 61.6 64.8 29.8 65.3 64.9 65.6 63.8 26.6 63.9 65.7 +Avg 28.1 47.9 56.3 63.5 53.7 62.4 62.1 67.8 36.3 68.4 68.1 68.8 65.8 31.6 66.0 67.2 + + +Table 19: Recall@20 on MKQA (Longpre et al., 2021) dataset for cross-lingual retrieval in all 25 languages. The +All of M3-Embedding denotes the hybrid retrieval result of dense, sparse, and multi-vec scores. + + Model Avg ar bn en es fa fi fr hi id ja ko ru sw te th zh de yo + BM25 31.9 39.5 48.2 26.7 7.7 28.7 45.8 11.5 35.0 29.7 31.2 37.1 25.6 35.1 38.3 49.1 17.5 12.0 56.1 + mE5large 65.4 76.0 75.9 52.9 52.9 59.0 77.8 54.5 62.0 52.9 70.6 66.5 67.4 74.9 84.6 80.2 56.0 56.4 56.5 + mE5base 60.13 71.6 70.2 51.2 51.5 57.4 74.4 49.7 58.4 51.1 64.7 62.2 61.5 71.1 75.2 75.2 51.5 43.4 42.3 + E5mistral-7b 62.2 73.3 70.3 57.3 52.2 52.1 74.7 55.2 52.1 52.7 66.8 61.8 67.7 68.4 73.9 74.0 54.0 54.0 58.8 + OpenAI-3 54.9 - - - - - - - - - - - - - - - - - - + BGE-M3-Dense 67.8 78.4 80.0 56.9 55.5 57.7 78.6 57.8 59.3 56.0 72.8 69.9 70.1 78.6 86.2 82.6 61.7 56.8 60.7 + BGE-M3-Sparse 53.9 67.1 68.7 43.7 38.8 45.2 65.3 35.5 48.2 48.9 56.3 61.5 44.5 57.9 79.0 70.9 36.3 32.2 70.0 + BGE-M3-Multi-vec 69.0 79.6 81.1 59.4 57.2 58.8 80.1 59.0 61.4 58.2 74.5 71.2 71.2 79.0 87.9 83.0 62.7 57.9 60.4 + BGE-M3-Dense+Sparse 68.9 79.6 80.7 58.8 57.5 59.2 79.7 57.6 62.8 58.3 73.9 71.3 69.8 78.5 87.2 83.1 62.5 57.6 61.8 + BGE-M3 All 70.0 80.2 81.5 59.8 59.2 60.3 80.4 60.7 63.2 59.1 75.2 72.2 71.7 79.6 88.2 83.8 63.9 59.8 61.5 + mGTE-TRM Dense 62.1 71.4 72.7 54.1 51.4 51.2 73.5 53.9 51.6 50.3 65.8 62.7 63.2 69.9 83.0 74.0 60.8 49.7 58.3 + mGTE-TRM Sparse 55.9 66.5 70.4 35.6 46.2 40.0 47.6 66.5 39.8 48.9 47.9 59.3 64.3 47.1 59.4 83.0 70.5 73.7 39.9 + mGTE-TRM Dense+Sparse 63.5 73.4 75.1 49.9 57.6 62.7 52.0 74.7 53.5 56.4 52.8 67.1 66.7 63.5 69.5 85.2 75.8 58.4 58.8 + + mGTE-reranker 68.5 77.1 63.1 78.6 56.3 72.4 80.3 79.6 58.6 59.1 74.6 75.5 59.4 56.3 56.5 62.2 72.2 86.3 65.1 + +Table 20: Multi-lingual retrieval performance on the MIRACL (Zhang et al., 2023b) dev set (measured by +nDCG@10). + + + + + 19 + Cli- CQA- MS + Argu- DB- Hotpot- NF- Sci- Sci- Touche- Trec- +BEIR Avg. mate- Dup- Fever FiQA MAR- NQ Quora + Ana Pedia QA Corpus docs fact 2020 Covid + Fever Stack CO +gte-Qwen2-7B-instruct 60.25 64.27 45.88 46.43 52.42 95.11 62.03 73.08 45.98 40.6 67 90.09 28.91 79.06 30.57 82.26 +NV-Embed-v1 59.36 68.2 34.72 50.51 48.29 87.77 63.1 79.92 46.49 38.04 71.22 89.21 20.19 78.43 28.38 85.88 +gte-Qwen2-1.5B-instruct 58.29 69.72 42.91 44.76 48.69 91.57 54.7 68.95 43.36 39.34 64 89.64 24.98 78.44 27.89 85.38 +voyage-large-2-instruct 58.28 64.06 32.65 46.6 46.03 91.47 59.76 70.86 40.6 40.32 65.92 87.4 24.32 79.99 39.16 85.07 +neural-embedding-v1 58.12 67.21 32.3 49.11 48.05 89.46 58.94 78.87 42 42.6 68.36 89.02 27.69 78.82 24.06 75.33 +GritLM-7B 57.41 63.24 30.91 49.42 46.6 82.74 59.95 79.4 41.96 40.89 70.3 89.47 24.41 79.17 27.93 74.8 +e5-mistral-7b-instruct 56.89 61.88 38.35 42.97 48.89 87.84 56.59 75.72 43.06 38.62 63.53 89.61 16.3 76.41 26.39 87.25 +google-gecko 55.7 62.18 33.21 48.89 47.12 86.96 59.24 71.33 32.58 40.33 61.28 88.18 20.34 75.42 25.86 82.62 +text-embedding-3-large 55.44 58.05 30.27 47.54 44.76 87.94 55 71.58 40.24 42.07 61.27 89.05 23.11 77.77 23.35 79.56 +gte-en-large-v1.5 57.91 72.11 48.36 42.16 46.3 93.81 63.23 68.18 42.93 36.95 56.08 89.67 26.35 82.43 22.55 77.49 +gte-en-base-v1.5 54.09 63.49 40.36 39.52 39.9 94.81 48.65 67.75 42.62 35.88 52.96 88.42 21.92 76.77 25.22 73.13 +BM25 41.7 31.5 21.3 29.9 31.3 75.3 23.6 60.3 22.8 32.5 32.9 78.9 15.8 66.5 36.7 65.6 +mE5-large 51.43 54.38 25.73 39.68 41.29 82.81 43.8 71.23 43.7 33.99 64.06 88.18 17.47 70.41 23.39 71.33 +mE5-base 48.88 44.23 23.86 38.52 40.36 79.44 38.17 68.56 42.27 32.46 60.02 87.65 17.16 69.35 21.35 69.76 +BGE-M3 Dense† 48.34 53.95 29.52 39.09 39.80 81.38 41.30 69.44 38.32 31.43 60.60 88.57 16.39 64.36 22.63 55.59 +BGE-M3 Sparse† 38.30 25.08 24.69 27.51 23.21 88.36 26.79 68.45 19.59 27.5 17.98 73.82 8.89 64.37 30.26 48.00 +BGE-M3 Dense+Sparse† 49.41 53.88 30.21 39.10 39.89 81.24 40.25 70.11 37.62 32.53 59.58 88.62 15.59 65.74 31.12 55.67 +mGTE-TRM Dense 51.07 58.36 34.83 38.12 40.11 92.07 44.99 63.03 39.92 36.66 58.10 88.02 18.26 73.42 22.76 57.4 +mGTE-TRM Sparse 39.24 40.06 24.17 25.11 20.0 88.32 28.58 64.68 19.39 28.34 19.71 76.84 10.92 67.72 21.52 53.33 +mGTE-TRM Dense+Sparse 51.43 58.48 34.89 38.36 39.72 93.14 44.98 65.01 39.99 36.67 56.90 89.05 18.26 73.45 24.09 58.46 ++ mGTE-reranker 55.42 58.53 44.93 38.37 45.62 93.9 44.38 74.51 44.99 36.29 65.21 81.67 18.42 75.59 31.29 77.75 +BGE-M3-unsupervised† 42.26 59.07 23.05 38.10 31.16 59.15 36.57 53.39 27.79 30.67 39.69 86.38 15.08 61.26 17.62 54.90 +mGTE-CPT-512,1024 44.93 52.99 17.93 45.01 37.63 34.13 48.38 54.39 31.76 39.01 48.48 86.82 22.95 72.46 18.56 63.46 +mGTE-CPT-8192 44.46 55.14 15.85 44.73 38.74 27.42 47.45 55.93 31.79 38.62 49.27 86.81 22.72 73.08 17.08 62.27 + + +Table 21: BEIR benchmark (Thakur et al., 2021) nDCG@10 scores. We include top models from MTEB Retrieval +English leaderboard. † Denote our runs. + + + + + Model Param. Dim. Seq Avg. Tau Scr. Tau Gov. Tau QMS. QASP. Tit. Art. QASP. Abs. Art. + Jinabase-v2 (Günther et al., 2023) 137M 768 8192 85.5 93.3 98.6 40.8 95.1 99.3 + nomic-embed-text-v1 (Nussbaum et al., 2024) 137M 768 8192 85.5 90.9 97.8 44.2 94.9 99.9 + text-embedding-3-small - 1536 8192 82.4 92.2 97.7 27.4 95.9 98.9 + text-embedding-3-large - 3072 8192 79.4 88.0 93.6 25.5 93.2 96.8 + mGTE-en-base-embed 137M 768 8192 87.4 91.8 98.6 49.9 97.1 99.8 + mGTE-en-large-embed 434M 1024 8192 86.7 92.6 98.7 44.5 97.8 99.8 + mE5base (Wang et al., 2024b) 279M 768 512 72.2 68.9 87.6 30.5 85.1 88.9 + mE5large (Wang et al., 2024b) 279M 1024 512 74.3 70.4 89.5 37.6 89.5 85.4 + E5mistral (Wang et al., 2024a) 7B 4096 4096 87.8 95.9 98.3 46.8 98.4 99.8 + BGE-M3-Dense† (Chen et al., 2024) 568M 1024 8192 84.9 93.8 97.4 41.9 93.2 98.3 + BGE-M3-Sparse† (Chen et al., 2024) 568M 1024 8192 84.9 95.5 97.9 46.7 85.7 98.9 + BGE-M3-Dense+Sparse† (Chen et al., 2024) 568M 1024 8192 87.4 97.7 98.2 47.7 93.6 99.7 + mGTE-TRM Dense 434M 768 8192 88.9 95.1 97.7 58.5 94.6 98.7 + mGTE-TRM Sparse 434M 768 8192 88.1 97.6 97.9 60.1 85.5 99.2 + mGTE-TRM Dense+Sparse 434M 768 8192 91.3 98.2 98.3 66.5 94.6 98.7 + + + Table 22: The nCDG@10 scores on the LoCo benchmark (Saad-Falcon et al., 2024). † Denote our runs. + + + + + 20 + \ No newline at end of file diff --git a/wizard-work/sphinx/archive-longctx-retrieval-2026-10-26/text/2409.13695v1.txt b/wizard-work/sphinx/archive-longctx-retrieval-2026-10-26/text/2409.13695v1.txt new file mode 100644 index 0000000..776d456 --- /dev/null +++ b/wizard-work/sphinx/archive-longctx-retrieval-2026-10-26/text/2409.13695v1.txt @@ -0,0 +1,780 @@ + Y OU O NLY U SE R EACTIVE A TTENTION S LICE + FOR L ONG C ONTEXT R ETRIEVAL + + + + Yun Joon Soh 1 Hanxian Huang 1 Yuandong Tian 2 Jishen Zhao 1 + + + A BSTRACT + Supporting longer context for Large Language Models (LLM) is a promising direction to advance LLMs. As +arXiv:2409.13695v1 [cs.CL] 3 Sep 2024 + + + + + training a model for a longer context window is computationally expensive, many alternative solutions, such + as Retrieval Augmented Generation (RAG), have been used. However, most existing RAG methods adopt + embedding-based retrieval that falls short on long contexts. + To address such challenges, we propose an attention-based retrieval technique, You Only Use Reactive Attention + slice (YOURA). YOURA leverages a novel retrieval heuristic called reaction score to rank the relevance of each + sentence in the input context with the query sentence. Intuitively, we measure how the per-token attention score + “reacts” to the query and greedily retrieves the most reactive sentences. Internally, YOURA generates a token- + indexed vector (called reaction vector) for the whole input context. To map each sentence to the token-indexed + vector, we propose an Embedding-Agnostic Sentence Yield (EASY), a best-effort token wiggling algorithm. + We evaluate our retrieval technique on three open-source pre-trained LLM models across six LongBench QA + datasets. Our technique achieves up to 30% vLLM inference throughput improvement for serving long-context + queries — with a nearly identical quality score to the simple yet effective truncate-middle approach (Bai et al., + 2023). + + 1 I NTRODUCTION a. Input Context & Query c. Our proposal vs. embeddig-distance based + + C1: A woman is wearing a red dress. Current Our Proposal + Pre-trained Large Language Models (LLM) are widely ex- S2: She is walking on t he st reet . + Emb. Distance Reaction Score + + plored for various Natural Language Processing (NLP) tasks. S3: Her name is Mary. + S1 S2 S3 S1 S2 S3 + + + To enhance the model for more complex tasks, researchers + Q: Where is Mary? + seek ways to extend the LLM’s context window size via Retrieve Retrieve + Semantically Close Most reactive + fine-tuning the pre-trained model (Chen et al., 2023b; Man- b. Performance Teaser + grulkar et al., 2022), proposing advanced attention mecha- Augmented Augmented + nisms (Xiao et al., 2024; Zhang et al., 2023), and improving Quality Her name is Mary. + Where is Mary? + She is walking on t he st reet . + Where is Mary? + +3% / +9% / +11% + Retrieval Augmented Generation (RAG). However, fine- Llama2 Llama3 Mistral + + tuning requires additional computational power for each Generated Generated + Inference Mar y i s i n t he + pre-trained model and is often targeted for specific tasks Throughput l i v i ng r oom. + On t he St r eet + + + or datasets. Advanced attention mechanisms conceptually +2% / 13% / 31% + Llama2 Llama3 Mistral + Context unaware, + generates incorrect + Correct response + with a better + extend the context window by selectively choosing a subse- responses. context-awareness + + quence of tokens for attention but are susceptible to details + due to the nature of dropping information at the token level. + RAG splits the contextual text into smaller chunks and re- Figure 1. YOURA improves the retrieval quality and inference + trieves a subset of chunks before inferencing based on the throughput by retrieving only the “reactive” sentences. + semantical distance to the query in the embedding vector + 1 + Department of Computer Science and Engineering, Univer- + sity of California, San Diego, San Diego, USA 2 Meta AI (FAIR), space. RAG’s retrieval nature has many benefits; it reduces + Menlo Park, California, USA. Meta-affiliated author served in + an advisory role and all data processing occurred at the Univer- + the input size to process, which reduces the computation dur- + sity of California, San Diego. Correspondence to: Jishen Zhao ing inference (Luo et al., 2024) and improves the accuracy + . by eliminating distracting information (Catav, 2023). + A high-quality retrieval is crucial for RAG but often suf- + fers from lower accuracy because (1) the common words + in both the query and text chunk put the semantic distance returns token indices for each sentence. +closer and (2) the semantically identical, but alphabetically + We evaluate (1) how much YOURA improves the answer +different words distance the query and text chunk in the + quality for the 6 LongBench QA datasets (Bai et al., 2023), +embedding vector space. For example, the similarity dis- + (2) how YOURA reduces the inferencing computation cost +tance between a query (“Where is Mary?”) and a sentence + by retrieving fewer, yet sufficient tokens for proper question- +(“Her name is Mary”) may be closer to a sentence (“She is + answering, and (3) how well our EASY algorithm maps +at home”) resulting in suboptimal retrieval. 1 + raw texts to the token-indices. Without any pre-training or +We propose a fine-tuning-free, attention-based retrieval strat- additional embeddings, YOURA shows a higher retrieval +egy, which can be applied to various off-the-shelf pre-trained ratio (context token count / retrieved token count) for open- +LLMs to achieve improved text generation quality. Our key source pre-trained models (Llama2, Llama3, Mistral) by up +insight is that the attention in a pre-trained model already to ×1.3 and still achieves a higher answer quality than prior +contains information about which tokens are important for embedding-based retrieval approaches. With the smaller +the given query. Figure 2 illustrates the difference between number of retrieved tokens, YOURA improves up to 30% +the embedding-based retrieval and our proposal; (a) is a typi- vLLM serving throughput. Our EASY algorithm shows an +cal embedding-based retrieval where embedding vectors for average of 93.7% sentence-to-token-index mapping accu- +both the query and context chunk list are generated through racy. +an embedding layer. Figure 2 (b) is another simple yet effec- + We contribute the following. +tive alternative to the embedding distance-based approach, +truncate-middle. Since the introduction and conclusion of • We propose You Only Use the Reactive Attention +most documents contain the most important information, slice (YOURA) algorithm, a fine-tuning-free, attention- +the truncation approach naively takes out the middle of the based retrieval algorithm. YOURA improves QA qual- +context so that the remaining text fits within the LLM’s ity by up to 10% while improving inference throughput +context window size. Unlike embedding-based retrieval or by up to 30%2 . +truncation, we propose an attention-based retrieval. + • We propose an Embedding-Agnostic Sentence Yield +Our proposed technique, You Only Use Reactive Attention (EASY) algorithm, which can map individual sen- +slice (YOURA) Figure 2 (c), is a novel attention-based tences to their vector slice at 93% accuracy. +retrieval technique. Anonlogically speaking, we use the +attention vector generated without query as the baseline • We provide a detailed analysis of YOURA’s QA quality +and measure how each token “reacts” to the query. We improvement on LongBench QA datasets. +refer to the “reactiveness” as Reaction Vector, an absolute +difference in attention scores calculated with and without the 2 R ELATED W ORK +query. The per-sentence average of the reaction vector called +reaction score, is a good ranking metric when retrieving Long-context Language Models. Training LLMs on se- +relevant sentences, as we show in our evaluation. quences with a maximum length while still ensuring they + infer well on sequences longer than the maximum is chal- +Properly slicing the reaction vector into sentences is cru- + lenging. Previous studies proposed techniques such as posi- +cial because YOURA splits the token sequence into chunks + tional interpolation (Chen et al., 2023a; Li et al., 2023), posi- +(as opposed to prior works, which split the raw text into + tional extrapolation (Press et al., 2021), external emory (Wu +chunks). Such a difference sparks a new challenge: map- + et al., 2020; Martins et al., 2021), memory-retrieval augmen- +ping sentences to a token sequence. Merely associating + tation strategies (Mohtashami & Jaggi, 2023; Wang et al., +specific token values (e.g., punctuation token) to a sentence + 2024) to push the limit of the context window size to 2 +boundary is insufficient because different tokenizer uses + million and even longer (Ding et al., 2024; Bertsch et al., +different vocabulary. For example, a tokenizer may encode + 2024). Although these works improved model accuracy on + .The (a sentence boundary with a missing white space) as + various tasks, the large context window size implies more +two tokens: [ .T , he ]. In such case, .T token becomes computational and memory costs. +part of the two consecutive sentences. Treating the token be- +fore or after .T token as a sentence boundary would easily Retrieval-Augmented Generation. Retrieval- +garble the remaining sentence splits. For this challenge, we Augmented Generation (RAG) retrieves relevant document +propose the Embedding-Agnostic Sentence Yield (EASY) chunks from the external knowledge base. Prior works +— a best-effort token wiggling algorithm — that takes a list integrate RAG with language models for question an- +of raw sentences and a single token sequence as input and swering with long documents (Stelmakh et al., 2022) + 1 + Measured with Sentence-Transformer “All-MiniLM-L6-V2”, 2 + The code has been open-sourced and is available at +scipy.spatial.distance. https://github.com/yjsoh/youra. + 2 + Context Query Context Query Context Query + + + Chunk ... Middle Truncation Transformer + + + Embedding Reaction Vec. Emb. + Head Truncated Tail Agnostic + Sentence + EmbChunk ... EmbQ + Chunks ... Yield + Less + important + Top-K w/ Cos. Dist. Top-K w/ React. Score + + + LLM Context Window Size LLM Context Window Size LLM Context Window Size + a. Embedding-based Retrieval b. Truncate Middle Approach c. Our Proposal: YOURA + + + +Figure 2. Comparison of a typical embedding-based retriever (cosine similarity distance), truncation-based approach, and our attention- +based retriever (reaction score). In our evaluation, we show that reaction score is a better retrieval heuristic in terms of quality and +performance. Unlike the embedding-based approach, which splits the raw string context into chunks (e.g., sentences) before retrieval, +YOURA splits the token-indexed vector, reaction-vector in (c), into chunks. Such an approach requires mapping the raw sentences to +token indices. We propose Embedding Agnostic Sentence Yield (EASY) algorithm for the challenge. + + +and in open-domain (Giorgi et al., 2022). Furthermore, served that the small model is capable of identifying the +language models adopted retrieval at various stages such key relevant information, and devised a way to mitigate the +as pretraining (Wang et al., 2023; Izacard et al., 2023; “positional bias” due to the positional embeddings by shuf- +Borgeaud et al., 2022), fine-tuning (Jiang et al., 2022; fling the long context. Unlike their approach, which relies +Zhang et al., 2024) and inference stage (Khandelwal et al., on a relatively complex perplexity calculation, our approach +2019). merely simply takes the mean within the attention matrix. + +Attention Mechanism. Researchers proposed self- 3 YOU O NLY U SE R EACTIVE ATTENTION +attention alternatives to mitigate its quadratic complexity, (YOURA) +which becomes a computational bottleneck for a long +context. These include sparse attention mechanisms with Limitation of Embedding Vectors — Context-Unaware +pre-defined sparsity patterns (Zhu et al., 2021; Liu et al., The limitation of embedding-based retrieval is that the input +2023), recurrence-based method (Bulatov et al., 2022), texts are the sole variable when generating the embedding +low-rank projection attention (Zhao et al., 2024), and vectors. For example, consider the following two paragraphs +memory-based mechanisms (Lou et al., 2024). However, each with two sentences: “Her name is Mary. She is at +these approximate methods introduce inductive bias (e.g., home.” and ”Her name is Alice. She is at home.”. In both +pre=defined sparsity) that can fit well for specific domains, cases, ”She is at home” is embedded into the identical vector, +but may reduce model quality in general LLM training. resulting in equal distance to any sentence. + +Fine-tuning for Long Context. Recent works (Ding et al., +2024; Chen et al., 2023b; Peng et al., 2023) show that a +pre-trained LLM context window can be extended by fine- Attention-Based Retrieval — Context-Aware We pro- +tuning on longer texts. However, fine-tuning approaches pose an attention-based heuristic for context-aware retrieval. +still require multiple self-attention computations demanding At a high level, our proposal is measuring how the query sen- +both high-quality training datasets and computational costs. tence impacts the language model’s attention distribution. +Our approach does not require fine-tuning. We observe that attention values of relevant information + change more drastically than irrelevant information. From +Context Compression. LongLLMLingua (Jiang et al., this observation, we introduce reaction vector to quantify +2023) is one of the closest approaches to ours; they ob- the attention shift caused by the query sentence. + 3 + Design Overview Figure 3 illustrates the overview of our Reaction Score Given the reaction vector, we define the +proposed design. The left portion of the figure depicts how reaction score (rs) as follows for a vector slice represented +a reaction vector is calculated and the right portion depicts with left-right index, [l, r): +how a reaction vector is used for the retrieval and how the +language model leverages the retrieved sentences to answer ReactionScore(ReactionVector, l, r) +the query. + = avg(ReactionVector[l : r]) (6) +3.1 Problem Statement & Definitions +Annotations We annotate ZC ∈ Rd×c , ZQ ∈ Rd×q as Problem Statement We define the retrieval task as finding +the continuous representation of the context and question, a set of non-overlapping chunks S = {(li , ri ) | 0 ≤ li ≤ +respectively, and d is the pre-trained model’s hidden dimen- ri ≤ |Zctxt |, ri ≤ li+1 }, such that +sion, c is the context token count and q is the question token +count. + argmaxS AnsQuality(LLMInference(concat(S , q))) (7) +Reaction Vector We define the reaction vector as follows: + where: + C Q + ReactionVector(Z , Z ) += abs AttnVec(ZC ) − AttnVec(concat(ZC , ZQ ))1:c +  + (1) • concat(S , q) denotes the concatenation of the chunks + in S with the query q. +where + • LLMInference is the function that processes the con- + AttnVec(Z) = Meancol (AttnMatrix(Q, K)) (2) catenated chunks and the query using a language + model. +AttnMatrix is the dense attention function before multiply- +ing the value projection and Q, K are the query and key • AnsQuality measures the quality (e.g., F1 Score) of +projection of Z, respectively, similar to the description in the answer produced by the inference function. +the “Attention is all you need” paper (Vaswani et al., 2023): + QK T 3.2 Embedding-Agnostic Sentence Yield (EASY) +   + AttnMatrix(Q, K) = softmax √ Algorithm + d + (3) + Q = Z × WQ Challenge Given a token sequence, finding the token in- + K =Z×W K dex of sentence boundary is non-trivial for two reasons: (1) + the embedding model for sentence split task (e.g., stanza) is + different from the model’s embedding, and (2) embedding +Meancol takes the attention matrix and returns a per-column + models with huge dictionary may result in an impossible +mean vector (note the subscription 1 : c to indicate trunca- + token sequence to achieve a perfect sentence split. For ex- +tion from 1 to c-th column): + ample, widely known sentence-splitting algorithms (e.g., + Stanza (Qi et al., 2020)) use a different dictionary from the + d d d + ! + 1 X X X + Meancol ([aij ]d×n )1:c = ai1 , ai2 , ..., aic LLM models (e.g., Llama3-8B-Instruct). Thus, the total + d i=1 i=1 i=1 token count varies across the embedding models (Table 1). + d + !c + 1 X Another challenge is that some models prefer huge dictio- + = aij + d i=1 naries resulting in a token sequence with an impossible case + j=1 + (4) to articulate a sentence boundary. For example, llama-3 + treats .T as a single token. Although it may be useful in +For multi-head attention, the reaction vector is a mean across code generation, a missing whitespace between a punctua- +heads: tion period and a subsequent sentence that starts with the + H d + !c word “The” would result in an impossible case. In other + h +  1 X 1X h + Meancol,heads [aij ]d×n 1:c = a words, Stanza would create a token sequence ( . - The ), + H d i=1 ij + h=1 j=1 whereas Llama3 tokenizer would output ( .T - he ). In + H d + !c such a case, an exact sentence boundary for Llama3 cannot + 1 X 1 X + = ah be expressed as a token index. For these reasons, finding + H d i=1 ij the sentence boundary token index across the embedding + h=1 j=1 + (5) model is non-trivial. + 4 + Our Proposal - You Only Use Reactive Attention (YOURA) Section 3.3 Embedding-Agnostic Sentence Yield (EASY) + : Maps raw sentence text to index of token sequence + + Context Only Attention Matrix Context + Query Attention Matrix Retrieval + I Token Reaction Score + Sentence + am Index : mean of reaction vector slice + (higher = relevant to query) + an I am an + amazing [ 0, 7 ) Mean ( ) + amazing + researcher researcher. Reaction Ignore + . + Vector Slice + I I like + like [ 7, 11 ) Mean ( ) + LLM. + LLM + . Retrieved! (? higher reaction score) + Who + am Augmented + I Query: "Who am I?" + ? Prompt: "Answer the question. {Retrieved Context} {Q}" + + Augmented Prompt + + Reaction Vector Generation + Ignore + = abs ( - ) Language Models + Llama2, Llama3, Mistral + Answer: + Amazing researcher! + Attention Vec w/o Query Attention Vec w/ Query + = Meancol (Attention Matrix) = Meancol (Attention Matrix) + + + + +Figure 3. Overview of YOURA and where it is used in the Retrieval Augmented Generation (RAG) with an example (example context: ”I +am an amazing researcher. I like LLM.”, example query: ”Who am I?”). The first step is calculating the reaction vector, an absolute +difference between the attention vector with and without the query (left side of the figure). The highlighted cells in the attention matrix +indicate that the token pair exhibits a relatively high value (e.g., Who vs. I). Once the reaction vector has been calculated, each sentence is +assigned a reaction score, the mean of a corresponding reaction vector slice. To map each sentence to a token sequence, we propose +Embedding-Agnostic Sentence Yield (EASY) algorithm (Section 3.2). The retriever passes on the sentences with high reaction scores to +the augmentor. The pre-trained LLM models generate answers using the augmented text, which includes the task-specific prompt, the +retrieved context, and the question. + researcher + + + like + LLM + amazing + I + am + + + + . + I + + + . + an + + + + +Embedding-Agnostic Sentence Yield (EASY) To re- 3.3 Retrieval using Reaction Score — End-to-end +solve the problem, we propose the Embedding-Agnostic + The retrieval algorithm starts with calculating the reaction + researcher + + + like + LLM + amazing + + + + + ? + Who + I + am + + + + . + I + + + . + + am + I + an + + + + +Sentence Yield (EASY) algorithm. The input to the algo- + vector. If the input context is longer than the model’s context +rithm is a single token sequence and a list of sentence strings + window size, we calculate the attention vector for each +acquired from the LLM tokenizer and the sentence splitting + chunk of context window size and concatenate them before +NLP models (e.g., Stanza (Qi et al., 2020)), respectively. + returning. At this point, we have the reaction vector whose +The EASY algorithm is a repetition of guessing the sen- length equals the input context’s token sequence. +tence boundary and making best efforts adjustments. First, + To slice the reaction vector for each sentence, we apply +EASY estimates a candidate index as the encoded length of + the Embedding-Agnostic Sentence Yield (EASY) algorithm +sentences that are already processed (Algorithm 1 line 7). + (Section 3.2). Then the retriever slices the reaction vector +EASY decodes the token subsequence between the most- + and calculates the geometric mean of the reaction vector +recently confirmed boundary index (m in Algorithm 1) + slice using the output of the EASY algorithm, a list of +and the current candidate and compares it against the + sentence boundary indices. At this point, we have a list of +target sentence (Algorithm 1 line 16). Based on the compar- + sentences and their associated reaction scores, which will +ison outcome (MATCH, INCLUDED, NO-MATCH), EASY + be used as a retrieval heuristic. +wiggles (+1 or -1) the candidate index until the decoded +subsequence matches the target or a termination condition For the actual retrieval, we greedily add sentences starting +is met (lines 17-23). Lastly, EASY returns the list of inte- with the highest reaction score until the retrieval token bud- +gers, where each value is a token index corresponding to a get is depleted or the sentence count threshold is reached +sentence boundary. If for some reason, the best effort fails, (80% of total sentence count). The retrieved sentences are +EASY restores the initial candidate index and appends to the reordered according to their original position as the final +boundary list. This retains the one-to-one mapping between cleanup step. +the target sentence and the boundary token index. + 5 + Dataset ID Source Avg # Tokens Metric # Data + Llama2 Llama3 Mistral + Single-Document QA + NarrativeQA 1-1 Literature, Film 35937 29777 35937 F1 200 + Qasper 1-2 Science 5603 4922 5517 F1 200 + MultiFieldQA-en 1-3 Multi-field 8046 6889 7877 F1 150 + Multi-Document QA + HotpotQA 2-1 Wikipedia 15244 12780 14890 F1 200 + 2WikiMultihopQA 2-2 Wikipedia 8381 7096 8281 F1 200 + MuSiQue 2-3 Wikipedia 18459 15544 18069 F1 200 + + Table 1. Evaluated dataset details. + + +4 E XPERIMENTS chunks. We measure the cosine similarity between + each chunk’s embedding vector generated by the +We evaluate how the YOURA improves the answering per- “paraphrase-MiniLM-L6-v2” model3 . +formance of 5 pre-trained models on LongBench (Bai et al., +2023) single-doc and multi-doc QA datasets. • with Truncation We use LongBench’s prompt along + with its context truncation approach: the whole context, +Datasets We evaluate YOURA on the single and multi- if it fits within the pre-trained model’s context window +document QA (Table 1). size, or the concatenation of the context head and tail + with an equal context window budget, otherwise. +Models We used three different open-sourced LLM mod- +els: LLama2-7B, Llama3-8B, and Mistralv0.2-7B, where • with YOURA Our proposed system as described in +each model has a context window size of 4K, 8K, and 32K, Section 3. +respectively. + 4.1 Longbench QA Result +Inference Setup For inference, we used the vLLM (Kwon +et al., 2023). We used the LongBench (Bai et al., 2023) code How does YOURA impact the output quality? From Ta- +to evaluate the generated answers. ble 2, which shows the answer quality using three different + open-sourced models (Llama2, Llama3, and Mistralv0.2), +Machine Setup We measured the vLLM (Kwon et al., we make the following observations. (1) We observe that +2023) inference performance by slightly modifying the YOURA showed better overall answer quality than all of the +vLLM’s throughput benchmark (v0.5.3) code on a single retrieval approaches (BM25 and embedding-based, except +NVIDIA H100-80G GPU. We used the default vLLM set- for Llama2 w/ B50 70) at a higher retrieval ratio — i.e., +tings except for changing the ignore eos to false . retrieving less information from the long context. (2) When + compared to the truncate-middle approach, YOURA shows +Experiment Setup We compare the following scenarios better overall quality for Llama2 and Llama3, and slightly +to evaluate how the YOURA improves the output quality less (-0.15) for Mistral. +or performance. For each setup, as done with the Long- (2-1) When broken down into dataset types, we observe +Bench (Bai et al., 2023), we equalize the retrieval budget to that YOURA is better at multi-document QA than single- +3500, 7500, and 31500 for models with 4K, 8K, and 32K document QA for Llama2 and Llama3. We claim that Trunc. +context window sizes, respectively. truncates the key information to correctly answer multi- + document QAs, but the head and tail of a single document + • Model without context No context is passed to the include key information in many cases. As for YOURA, + LLM. the key information is retrieved properly for both the single- + doc and multi-doc QAs resulting in a comparable answer + • with BM25 Retrieval Bx y indicates chunking at quality. For YOURA’s lower single-document score, we + roughly x tokens and retrieving the top y chunks. conjecture that the retrieval budget (4k, and 8k for Llama2 + • with Embedding Retrieval Ex y indicates chunk- 3 + https://huggingface.co/sentence-transformers/paraphrase- + ing at roughly x tokens and retrieving the top y MiniLM-L6-v2 + 6 + Algorithm 1 EASY Algorithm Setup Retr. Ratio SDOC MDoc Overall +Require: seq - Encoded token sequence Avg Avg Avg Avg +Require: T S - Target Sentences generated by NLP models +Require: tol ← 30 - Max wiggling tries LLaMA2 INF 13.65 19.30 16.47 + 1: P ← [] {Processed sentences} w/ B500 7 3.28 22.82 20.17 21.49 + 2: B ← [] {Sentence boundary indices} w/ B50 70 3.28 23.86 23.25 23.56 + 3: i, m ← 0 {Initialize indices} w/ E500 7 3.28 17.13 19.57 18.35 + 4: while P ̸= T S do w/ E50 70 3.28 24.15 22.87 23.51 + 5: APPEND T S[i] to P w/ Trunc. 3.28 25.74 22.34 24.04 + 6: c ← Length(Encode(Concat(P ))) {Candidate} w/ YOURA 3.39 23.22 25.17 24.20 + 7: saved ← c + LLaMA3 INF 11.59 23.16 17.37 + 8: visited ← ∅ + w/ B500 15 1.57 34.07 33.27 33.73 + 9: while true do + w/ B50 150 1.57 32.92 35.83 34.17 +10: if c ∈ visited or c > |seq| or |c − saved| > tol + w/ E500 15 1.57 33.33 30.82 32.08 + then + w/ E50 150 1.57 31.50 35.70 33.60 +11: c ← saved + w/ Trunc. 1.57 36.83 34.72 35.93 +12: break + w/ YOURA 1.76 36.57 36.44 36.50 +13: end if +14: ADD c to visited Mistralv0.2 INF 7.02 11.47 9.25 +15: s ← Decode(seq[m : c]) w/ B500 63 1.04 32.29 24.66 29.02 +16: if s == T S[i] then w/ B50 630 1.04 29.14 23.28 26.63 +17: break w/ E500 63 1.04 26.79 22.06 24.76 +18: else if s is substring of T S[i] then w/ E50 630 1.04 29.14 23.28 26.63 +19: c←c+1 w/ Trunc. 1.04 32.65 26.62 29.64 +20: else w/ YOURA 1.33 33.14 25.84 29.49 +21: c←c−1 +22: end if + Table 2. Answer quality comparison for LongBench’s single- +23: end while + document and multi-document QA datasets. YOURA shows better +24: APPEND c to B + quality with a higher retrieval ratio — i.e., retrieved fewer tokens. +25: m←c All retrieval scenarios use the maximal budget (4K, 8K, and 32K +26: i←i+1 tokens depending on the model) except for without-context setup +27: end while and “YOURA”. The retrieval ratio (second column) is the average +28: return B of the total token count divided by the retrieved token count. + + + +and Llama3, respectively) is insufficient to retrieve all of the +relevant information. As a supporting fact, the quality differ- + From the observations, we conclude that the balance be- +ence between Trunc. and YOURA decreases significantly + tween the pre-trained model’s context window size and the +from -2.52 to -0.26. Experiment results for a pre-trained + retrieval ratio is important for long context QA. +model with a 16k context window size would be useful for +proving/disproving the conjecture but no solid 16k model is How does YOURA’s higher retrieval ratio improve in- +available at the time of the publication. ference performance? Table 3 shows the average vllm + inference throughput (requests per second) assuming offline +(2-2) For Mistral, which has a large context window size + truncation/retrieval. We make the following observations. +of 32k, truncation rarely happens (as shown in the small + First, YOURA improves the overall inference throughput, +retrieval ratio). Without truncation, distractful information + especially for models with larger context window sizes. This +is included as part of the augmented context resulting in + is because the relative retrieval ratio is larger for models +worse single-document answer quality than YOURA. 4 Our + with larger context window sizes. Second, the retrieval ra- +explanation for multi-document QA using Mistral, where + tio is a good relative throughput estimator. The relative +YOURA showed a slightly worse quality (-0.78), is sim- + throughput improvements (+2%, +13%, +31%) are on par +ilar to the explanation in (2-1); YOURA’s relatively high + with the relative retrieval ratio (+3%, +12%, +28%). We +retrieval ratio resulted in leaving out some key information. + conclude that the performance of LLM serving platforms + 4 + The impact of distractful information on output quality is such as vLLM is sensitive to the retrieved context size and +observed in other work (Catav, 2023) as well. underscores the importance of good information retrieval. + 7 + Mod Retrieval SDOC MDOC Overall Avg Mod 1-1 1-2 1-3 2-1 2-2 2-3 Avg + L2 Trunc. 5.25 5.76 5.50 L2 94.1 83.1 93.5 98.4 98.8 97.8 94.3 + L2 YOURA 5.43 5.78 5.60 (+2%) L3 99.9 99.6 96.5 86.5 86.9 85.6 92.5 + L3 Trunc. 3.23 2.79 3.01 M 94.0 83.1 93.5 98.4 98.8 97.8 94.3 + L3 YOURA 3.76 3.03 3.39 (+13%) Avg 96.0 88.6 94.5 94.4 94.8 93.7 93.7 + M Trunc. 1.97 1.36 1.66 L2: Llama2-7B, L3: Llama3-8B, M: Mistralv0.2 + M YOURA 2.54 1.82 2.18 (+31%) + Table 4. Evaluation of EASY algorithm’s average sentence match +Table 3. vLLM Serving performance (request per second, higher rate (exact match / total # of sentences) for each model and dataset +the better) for LongBench’s single-document and multi-document (Table 1) pairs. +QA datasets with respect to Truncation and YOURA. YOURA +retrieved fewer tokens resulting in up to 30% higher inference +throughput. Mod 1-1 1-2 1-3 2-1 2-2 2-3 Avg + L2 3.26 3.48 2.54 2.61 2.36 2.67 2.82 + NZ 5.40 4.80 4.35 4.22 4.13 4.31 4.54 +4.2 EASY Sentence Splitter Accuracy + L3 1.71 3.45 2.77 3.16 2.80 3.23 2.85 +How well can the EASY algorithm split tokens into sen- NZ 3.70 4.76 4.52 4.53 4.29 4.64 4.41 +tences? Table 4 shows the quality of our EASY sentence M 3.54 3.52 2.56 2.65 2.38 2.71 2.89 +splitting algorithm. We used Stanza (Qi et al., 2020), an NZ 5.52 4.85 4.40 4.29 4.17 4.38 4.60 +open-sourced NLP toolkit, to split raw text into sentences. +Then we ran the EASY algorithm for each tokenizer model L2: Llama2-7B, L3: Llama3-8B, M: Mistralv0.2 +to identify the sentence boundary within a token sequence. NZ* (Non-Zero): average of non-zero distances. +After decoding each token segment (delimited by the sen- +tence boundary index), it is a match if the Stanza’s sentence Table 5. Average Levenshtein distance between the target sentence +(namely target sentence) matches exactly after the cleanup and EASY algorithm’s sentence split. A smaller number indicates +(e.g., trailing whitespace). We report the average match rate fewer character edits (insert, remove, replace) to transform the + EASY output sentence to the target sentence, and thus a better +for each model and dataset pair. + quality split. +We make the following observations. Overall the EASY +algorithm can identify the sentence boundary for models +with varying dictionary sizes: Llama2, Llama3, and Mis- The NZ row in Table 5 is the average Levenshtein distance +tralv0.2 tokenizers with 32000, 128256, and 32000 words, of unmatched sentences. From these rows, we conclude +respectively. Llama2 and Mistral showed almost identical that the EASY algorithm is resilient to incorrect split and +match ratios, as both models’ tokenizers are based on simi- its cascading effect. +lar vocabulary. Llama3 uses roughly ×4 more vocabulary +resulting in fewer total token counts as shown in Table 1 5 C ONCLUSION +(e.g., .T into a single token). Due to the rich vocabulary, a +typo easily blurred sentence boundaries causing many im- We propose a simple, yet effective attention-based retrieval +perfect matches and thus the smallest overall match quality algorithm. We show that attention vectors are useful re- +as in the 2-1, 2-2, and 2-3 datasets. For 1-1, 1-2, and 1-3, trieval and propose novel retrieval heuristics called reaction +datasets, the rich vocabulary worked in favor. Many atypical scores. Our fine-tuning approach improves the output qual- +char sequences (e.g., , math formula) were present in ity or the inference performance of off-the-shelf models for +these datasets, which were properly represented with tokens long-context QA tasks. +using Llama3. + + +How resilient is the EASY algorithm from edge cases? +To quantify the resilience despite typos and overly represen- +tative dictionaries, we measured the Levenshtein distance +between the target sentence and the sentence yielded by +our EASY algorithm (Table 5). On average, one needs less +than 3 character edits (insert, remove, replace) to the EASY +output sentences to match the target sentences. + 8 + R EFERENCES Jiang, Z., Gao, L., Araki, J., Ding, H., Wang, Z., Callan, + J., and Neubig, G. 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Long-short trans- + former: Efficient transformers for language and vision. + Advances in neural information processing systems, 34: + 17723–17736, 2021. + + + + + 10 + A D ETAILED R ESULTS +Table 6, Table 7 are the detailed version of Table 2 and +Table 3, respectively. + + + + + 11 + Setup Retr. Rate Single-Document QA SDoc Multi-Document QA MDoc Overall + Avg 1-1 1-2 1-3 Avg 2-1 2-2 2-3 Avg Avg + LLaMA2 INF 8.89 13.87 18.19 13.65 23.05 25.67 9.17 19.30 16.47 + w/ B500 7 3.28 12.59 21.44 34.42 22.82 25.92 27.47 7.11 20.17 21.49 + w/ B50 70 3.28 16.37 21.51 33.71 23.86 27.48 28.62 13.64 23.25 23.56 + w/ E500 7 3.28 12.85 16.08 22.47 17.13 25.51 24.84 8.35 19.57 18.35 + w/ E50 70 3.28 17.15 20.12 35.18 24.15 28.80 27.15 12.66 22.87 23.51 + w/ Trunc. 3.28 18.84 21.71 36.68 25.74 27.55 31.08 8.40 22.34 24.04 + w/ YOURA 3.39 15.48 23.41 30.77 23.22 29.55 31.11 14.86 25.17 24.20 + LLaMA3 INF 9.62 14.38 10.78 11.59 28.43 30.46 10.60 23.16 17.37 + w/ B500 1.57 18.27 40.21 43.72 34.07 41.75 41.18 16.88 33.27 33.73 + w/ B50 1.57 19.86 36.51 42.39 32.92 47.04 40.58 19.86 35.83 34.17 + w/ E500 1.57 16.38 39.85 43.76 33.33 42.28 34.93 15.25 30.82 32.08 + w/ E50 1.57 19.05 35.44 40.02 31.50 48.05 36.85 22.19 35.70 33.60 + w/ Trunc. 1.57 21.54 44.21 44.75 36.83 46.43 36.23 21.50 34.72 35.93 + w/ YOURA 1.76 21.47 42.39 45.85 36.57 50.78 34.24 24.29 36.44 36.50 + Mistralv0.2 INF 7.24 7.69 6.13 7.02 16.41 11.87 6.13 11.47 9.25 + w/ B500 63 1.04 20.74 29.22 46.91 32.29 34.26 22.26 17.47 24.66 29.02 + w/ B50 630 1.04 18.84 25.08 43.50 29.14 32.85 18.66 18.32 23.28 26.63 + w/ E500 63 1.04 14.02 24.14 42.22 26.79 32.81 17.55 15.82 22.06 24.76 + w/ E50 630 1.04 18.84 25.08 43.50 29.14 32.85 18.66 18.32 23.28 26.63 + w/ Trunc. 1.04 20.80 29.23 47.92 32.65 37.28 21.72 20.86 26.62 29.64 + w/ YOURA 1.33 22.37 30.17 46.88 33.14 36.11 21.45 19.95 25.84 29.49 + The bold score indicates the best score for each pre-trained model. + +Table 6. Detailed experiment results for LongBench’s single-document and multi-document QA datasets. YOURA shows better quality +with a higher retrieval ratio — i.e., retrieved fewer tokens. All retrieval scenarios use the maximal budget (4K, 8K, and 32K tokens +depending on model) except for without-context setup and “YOURA”. Retrieval ratio (second column) is an average of the total token +count divided by the retrieved token count. + + + + + Model Retrieval Single-Document QA Multi-Document QA Overall Avg + Method 1-1 1-2 1-3 2-1 2-2 2-3 Req./Sec + LLaMA2 Trunc. 5.58 4.68 5.50 5.71 5.87 5.68 5.50 + LLaMA2 YOURA 5.70 4.88 5.72 5.70 6.00 5.63 5.60 (+2%) + LLaMA3 Trunc. 2.50 3.95 3.23 2.63 3.23 2.52 3.01 + LLaMA3 YOURA 2.60 4.87 3.82 2.72 3.83 2.53 3.39 (+13%) + Mistralv0.2 Trunc. 0.55 3.17 2.19 1.08 2.14 0.86 1.66 + Mistralv0.2 YOURA 0.64 4.02 2.97 1.45 2.85 1.15 2.18 (+31%) + +Table 7. vLLM Serving performance (request per second, higher the better) for LongBench’s single-document and multi-document QA +datasets with respect to Truncation and YOURA. YOURA retrieved fewer tokens resulting in upto 30% higher inference throughput. + + + + 12 + \ No newline at end of file diff --git a/wizard-work/sphinx/archive-longctx-retrieval-2026-10-26/text/2411.00744v2.txt b/wizard-work/sphinx/archive-longctx-retrieval-2026-10-26/text/2411.00744v2.txt new file mode 100644 index 0000000..b3c48af --- /dev/null +++ b/wizard-work/sphinx/archive-longctx-retrieval-2026-10-26/text/2411.00744v2.txt @@ -0,0 +1,1284 @@ + CARROT: A Learned Cost-Constrained Retrieval + Optimization System for RAG + Ziting Wang† , Haitao Yuan† , Wei Dong† , Gao Cong† , Feifei Li‡ + † Nanyang Technological University, Singapore + ‡ Alibaba Group, China + † ziting001@e.ntu.edu.sg, † {haitao.yuan, wei dong, gaocong}@ntu.edu.sg, ‡ lifeifei@alibaba-inc.com + + + + Abstract—Large Language Models (LLMs) have demonstrated Response: + impressive ability in generation and reasoning tasks but struggle Designer: The Eiffel Tower was named after + the engineer Gustave Eiffel, whose company + Query: "Who designed the Eiffel + with handling up-to-date knowledge, leading to inaccuracies or designed and built the tower. +arXiv:2411.00744v2 [cs.DB] 12 Mar 2026 + + + + + Tower and when was it + Construction period: It was constructed + hallucinations. Retrieval-Augmented Generation (RAG) mitigates constructed? Provide information + on its height as well." between 1887 and 1889. + this by retrieving and incorporating external knowledge into Height: The Eiffel Tower stands at 324 meters + tall. + LLMs + input prompts. In particular, due to LLMs’ context window + limitations and long-context hallucinations, only the most relevant Chunk1: "The Eiffel Tower in Paris + Token + held the title of the world's tallest Score + “chunks” are retrieved. However, current RAG systems face three structure for over 40 years until χ1 + key challenges: (1) chunks are often retrieved independently 1930.” 0.3 256 + χ1 + χ3 0.4 512 + without considering their relationships, such as redundancy and Chunk2:“It was the tallest man- + made structure in the world for 41 + ordering; (2) the utility of chunks is non-monotonic, as adding years until the completion of the + Reranker Model χ3 + χ4 0.8 512 + Chrysler Building in New York in + more chunks can degrade quality; and (3) retrieval strategies fail 1930. ” χ4 + χ3 0.9 512 + Score + to adapt to the unique characteristics of different queries. Chunk3:" The Eiffel Tower Chunk χ3 0.8 χ1 + χ2 + χ4 0.6 768 + constructed between 1887 and + To overcome these challenges, we design a cost-constrained 1889, it stands at 324 meters tall.” Chunk χ1 0.6 χ1 + χ4 + χ3 0.8 768 + retrieval optimization framework for RAG. We adopt a Monte Chunk4:"It was named after the Chunk χ4 0.4 + Carlo Tree Search (MCTS) based strategy to find the opti- engineer Gustave Eiffel, whose χ1 + χ2 + χ3+ χ4 0.9 913 + company designed and built the Chunk χ2 0.3 + mal chunk combination order, which considers the chunks’ item, even though several other + designs were considered initially." Chunk Combination Order + Chunk Scores + correlations. In addition, to address the non-monotonicity of Potential Chunks + + chunk utility, instead of treating budget exhaustion as the + termination condition, we design a utility computation strategy Fig. 1: Chunk combination order example. + to identify the optimal chunk combination without necessarily + exhausting the budget. Furthermore, we propose a configuration Q&A, as evidenced by results on knowledge-intensive tasks + agent that predicts optimal configurations for each query do- + main, improving our framework’s adaptability and efficiency. + and broad industry adoption [19], [20]. + Experimental results demonstrate up to a 30% improvement However, many existing RAG frameworks incur expensive + over baseline models, highlighting the framework’s effectiveness, computational costs. First, tuning-based RAG approaches [14], + scalability, and suitability. Our source code has been released at [21]–[24] require extensive instruction tuning to enhance + https://github.com/wang0702/CARROT. LLMs’ abilities in context evaluation and utilization. These + methods fine-tune models on specialized tasks like context + I. I NTRODUCTION ranking [14] or self-reflection [21] to improve retrieval quality + Although LLMs have demonstrated exceptional capabilities and generation accuracy. While effective, they demand signif- + in generative tasks, they still struggle with accessing up-to-date icant computational cost for model training and specialized + information [1]–[4], leading to hallucinations [5]. RAG has datasets. Similarly, graph-based RAG methods [6], [13], [25]– + emerged as a crucial solution to address this issue, especially [27] rely on LLM inference to construct knowledge graphs or + for tasks requiring updated external knowledge such as ques- guide actions. While these graph-based approaches avoid tun- + tion answering tasks [2], [5]–[7], personalized agents [8], and ing models, they impose significant computational overhead by + time series analysis [9], [10]. External data is often too large to requiring multiple LLM inferences for graph construction and + input directly into LLMs, so it is split into chunks stored in a chunk summarization, making them prohibitively expensive + vector database [11]–[15]. Users query these chunks to create when scaling to large knowledge bases. + prompts, making efficient and accurate search algorithms a Besides tuning-based and graph-based methods, rank-based + key research focus in the information retrieval domain [1]–[5], RAG represents another prevalent approach [12], [15], [28], + [16]. Note that although Agentic RAG [17], [18] establishes a [29]. These methods assign a utility score to each chunk, + new paradigm for complex, multi-step tasks, it does not render indicating its relevance for answering the user query. These + standard RAG obsolete and may rely on standard RAG for scores typically derive from similarity metrics between queries + subtasks. Moreover, standard RAG remains the default choice and chunks or from specialized reranker models [28], [30], + for efficient, single-turn information retrieval in enterprise [31], whose scores have demonstrated strong correlation with + final response quality in production deployments [32], [33]. + Corresponding author: Haitao Yuan (haitao.yuan@ntu.edu.sg) Unlike tuning-based and graph-based methods that require + User Query Embedding Vector DB Potential Configuration Agent Policy Tree Optimal Chunk + Chunks Order LLMs + Search + + + + Chunks Retrieval Online Configuration Inference + Optimal Chunk Combination Order Search + Reranker + Configuration + Chunks Agent Iterations + Candidates + Coefficient + + Positive Lconstrastive + Query Label + Lclassification MCTS-Based Tree Search + Negative + Label Encoding Network Lregression + Offline Contrastive Training + + + Fig. 2: The architecture overview of CARROT framework. + +substantial LLM modifications and interactions, rank-based hallucinations. For example, as illustrated in Figure 1, when +methods usually employ reranker models, offering reasonable χ3 and χ4 are selected, adding chunk χ1 decreases utility, +computational efficiency. However, our empirical study in highlighting that utility scores are often non-monotonic. +Table II reveals that existing rank-based methods underper- Challenge 3: Diversity of queries. Users’ queries have +form in difficult query workloads and long-context scenarios, different types, requiring different ranking strategies due to +presenting three key challenges. their unique characteristics [15], [37]. Current RAG systems + Challenge 1: Relationships between chunks. There are two predominantly rely on a single reranker model [30], [38] to +main methods to identify relevant chunks. The first uses determine chunks’ utility scores. Although various reranker +approximate k-nearest neighbor (AKNN) to rank and select models have been developed, their performance varies sig- +top-k chunks [34]. The second clusters chunks and returns nificantly by query types, with no single model consistently +all chunks in the most relevant clusters [6], [13]. However, outperforming others across all variations (as shown in our +AKNN disregards correlations entirely, and clustering treats all preliminary experiments in Fig. 5). Current RAG systems [12], +chunks in a cluster as equally relevant, leading to redundancy [28] lack the flexibility to adapt to varying query intentions. +when chunks have overlapping information. For example, in Problem Statement. Is there a RAG framework that achieves +Figure 1, querying the height and history of the Eiffel Tower both high efficacy and efficiency while maintaining acceptable +using the AKNN method would select χ3 and χ1 , which computational costs? +only provide historical details, failing to address the query Our contributions. We answer this question by proposing +fully. However, the clustering approach returns all chunks a novel rank-based RAG framework named cost-constrained +(χ1 , χ2 , χ3 , χ4 ), causing redundancy. The optimal solution retrieval optimization (CARROT). On one hand, CARROT is +is to select χ3 and χ4 , avoiding redundancy while meeting a rank-based framework, thus having much higher efficiency +the query’s intent. In addition, previous work [35], [36] than graph-based and tuning-based methods. On the other +demonstrates that information position significantly affects hand, it considers chunks correlation, non-monotonicity, and +RAG performance, a phenomenon known as “lost in the query intent diversity in the search process to enhance response +middle” [35]. For instance, placing χ4 before χ3 yields better quality. Additionally, to further reduce computational costs +results. However, finding the best chunk combination and order and make the framework practical, we minimize token cost +is challenging due to the exponentially growing search space, by incorporating cost constraints in the search process. +and we prove this problem is NP-hard in Section II. • We propose the first RAG framework that explicitly consid- + Challenge 2: Non-monotonicity of utility. Current solutions ers chunk combination order for the RAG task. Rather than +are typically based on the assumption that including more treating each chunk independently or at the cluster level, we +chunks will yield better final results. Specifically, in the design an MCTS-based policy tree search to identify the op- +AKNN-based approach, exactly k chunks are selected each timal chunk combination order. Unlike greedy methods that +time. In the clustering approach, a distance threshold between select the highest-scored chunk at each step, our approach +clusters and the query is fixed, and all clusters within this iteratively samples and evaluates different paths, leveraging +threshold are returned. Both of them return as many chunks a UCB-based utility function to balance exploring new +as possible. However, in practice, the utility of chunks is not combinations with exploiting promising ones. This design +monotonic. More specifically, excessive chunks can dilute key addresses the exponential search space by focusing compu- +information by adding marginally relevant content, creating tational resources on high-potential paths, while our parallel +noise that reduces clarity. Additionally, conflicting or nuanced evaluation strategy scores multiple candidates simultane- +differences across chunks may confuse the model, leading to ously for efficiency. Through these designs, we address + Challenge 1 while maintaining computational efficiency. as shown in Fig. 1, the order of chunks in this combination +• In contrast to existing RAG frameworks that treat budget critically affects RAG performance. Therefore, we incorporate + exhaustion merely as a termination criterion, we design the chunk combination order to denote it, and the objective is + a novel formulation that directly incorporates budget con- to determine the combination and ordering that yield the best + straints into the optimization of chunk combinations. Our benefit, formally defined as follows: + formulation explicitly captures the non-monotonic utility of + Definition 2 (Optimal Chunk Combination Order Selection). + chunks, effectively addressing Challenge 2. + Let Q denote the input query, {χ1 , χ2 , . . . , χk } the set of +• We propose a contrastive learning-based configuration agent + candidate chunks, B the cost budget, and W (Φ) the benefit + that dynamically predicts MCTS configurations per query, + of a chunk combination order Φ = ⟨χϕ1 , . . . , χϕm ⟩, where + adapting reranker models and configurations to the specific + ϕi indicates the position of each chunk in the sequence. The + query domain. This approach tailors retrieval for dynamic, + objective is to identify the optimal chunk combination order Φ̂ + domain-specific queries with flexibility and robustness, ad- + that maximizes the overall benefit under the cost constraint: + dressing Challenge 3. X +• We conducted comprehensive experiments comparing our Φ̂ = arg max W (Φ) s.t. cost(χi ) ≤ B (1) + Φ + framework against both graph-based and rank-based meth- χi ∈Φ + + ods. The results validate the effectiveness, efficiency, and Importantly, solving the above problem is non-trivial be- + scalability of our approach, demonstrating a 30% perfor- cause the benefit of a chunk combination cannot be obtained + mance improvement over baseline methods. by simply summing the individual benefits of its constituent + II. P RELIMINARIES chunks. Instead, the overall benefit must be evaluated by + invoking theP reranker model on the whole combination (i.e., + We first introduce the definitions of chunks and chunk W (Φ) ̸= χi ∈Φ W (χi )). To verify this, we conducted ex- +combination orders. We then formalize the chunk order opti- periments to analyze the non-additivity of chunk benefits, as +mization problem and give the NP-hard proof of the problem. detailed in our technical report [40], which demonstrates that +RAG & Chunks. RAG improves generation models by re- all tested reranker models exhibit non-additive scoring behav- +trieving relevant context from external corpora, which is first ior. Furthermore, the problem is computationally challenging, +divided into smaller, manageable units called chunks, typically as formally established by the following theorem. +stored in a vector database. The definition of chunk is given +as follows: Theorem 1. The Optimal Chunk Combination Order Selection +Definition 1 (Chunk). Let C represent a corpus of documents, problem is NP-hard. +and a chunk χ is defined as a contiguous block of text extracted The NP-hardness is proved via a polynomial-time re- +from C. Formally, a chunk χ consists of a sequence of tokens duction from the Maximum Weighted Hyperclique Problem +(t1 , t2 , . . . , tn ), where each ti is a token from C and the size (MWHP) [41], which has been shown to be NP-hard, as +n is set by users. discussed in the corresponding technical report [40]. +Chunk Cost & Benefit. Similar to prior work [39], the cost of +a chunk is defined as cost(χ) = |χ|, representing the number III. R ELATED W ORK +of tokens it contains; thus, longer chunks incur higher costs. 1) Retrieval-augmented Generation: RAG [1], [10], [28], +The benefit of a chunk reflects its estimated contribution to [42] is widely used to handle knowledge-intensive NLP tasks. +the final response quality. In RAG systems, each chunk is Current studies [13]–[15], [21] focus on how to optimize RAG +embedded into a vector representation using an embedding performance using different approaches, broadly classified as +model to capture its semantic information. Given a query Q, rank-based methods, graph-based methods, and tuning-based +the vector database performs a similarity search to identify the methods. In typical rank-based methods, a dense embedding- +most relevant chunks, which are then passed to the generator based retriever searches for relevant information from an +(e.g., a large language model) to produce the final response. external database, which is then used by the LLM generation +In this phase, we measure the benefit of a chunk by its esti- process. To improve this pipeline, some studies [12], [43] +mated contribution to the final response quality. We measure have focused on training retrievers to better suit the generation +this using a reranker model: W (Q, χ) = Reranker(Q, χ), needs of LLMs, developing multi-step retrieval methods, and +where the reranker is a neural model trained to predict the filtering out irrelevant information. Although there are many +relevance between query Q and chunk χ. For simplicity, we advanced retrievers [1], [29], [42], [44], it is more promising +use W (Q, χ) and W (χ) interchangeably when no ambiguity to optimize the retriever and LLM together in an end-to- +arises. end process [7], [45]. For example, research [46], [47] has +Chunk Combination Order. In an RAG system, retrieval focused on training retrievers and LLMs together, either si- +from the vector database often yields multiple chunks. How- multaneously or in stages. For graph-based methods [6], [13], +ever, due to input constraints and hallucination of the genera- [25]–[27], studies try to use knowledge graphs to optimize +tion model, incorporating all of them is infeasible. Thus, it is external knowledge organization. However, these methods +necessary to select an optimal subset, referred to as a chunk require frequent external LLM calls to generate the graph +combination, that fits within a given cost budget. Moreover, structure or guide actions, especially when the graph needs + to be re-indexed or updated with new knowledge frequently, To tackle these, we propose CARROT, a learned system that +which incurs high computational costs. Therefore, tuning- retrieves the optimal chunk combination under a given query +based methods such as [14], [21], [22] use instruction tun- and cost budget. Its core component, the Optimal Chunk Com- +ing to improve response comprehensiveness and enable the bination Search module, employs an MCTS-based policy tree +LLM to self-reflect or rank knowledge without frequent re- to sequentially explore chunk orders under cost constraints, +indexing. However, these approaches often overlook the high effectively modeling inter-chunk dependencies (Challenge 1) +computational cost in the tuning phase, which significantly and non-monotonic utilities (Challenge 2). Furthermore, the +impacts the practicality of RAG systems. To our knowledge, Configuration Agent module automatically selects the best +this paper is the first approach that considers the order of MCTS configuration and reranker for different query domains, +chunk combinations within computational cost constraints. addressing Challenge 3. We next overview these two modules. + 2) Reranking for RAG: Reranking methods [30], [38], [48], 1. Optimal Chunk Combination Search: A straightforward +[49] are crucial for improving retrieval performance in rank- approach to considering chunk correlations involves retrieving +based RAG methods, as they align retrieved chunks with LLM potential chunks from a vector database (Step 1 in Figure 2) +requirements and prioritize the content most conducive to and exhaustively exploring all possible chunk combinations. +generating high-quality responses. Traditional reranking ap- However, this method incurs significant latency and computa- +proaches [31], [50] typically rely on mid-sized language mod- tional costs. To mitigate this, we construct a policy tree (Step 2 +els, such as BERT or T5, to rank retrieved contexts. However, in Figure 2), reframing the optimal chunk combination search +these models often struggle to capture semantic relationships as a node search problem within the tree. Specifically, the +between queries and contexts, especially in zero-shot or few- root node of the policy tree represents an initial empty state, +shot settings. Therefore, recent tuning-based research [14], and each child node corresponds to a specific combination +[21], [22] highlights the potential of instruction-tuned LLMs of chunks. We design an MCTS-based search algorithm to +to improve context reranking by more accurately identifying address this problem, which iteratively constructs a policy tree +relevant contexts, even in the presence of noise or irrelevant for optimal chunk combination search. During the search pro- +information. Despite these advancements, the full capacity of cess, both cost and budget constraints are explicitly incorpo- +LLMs for reranking in RAG systems remains underutilized. In rated to guide decision-making. Each iteration of the algorithm +particular, studies have shown that the arrangement of chunks consists of four phases: (1) Selection: iteratively selecting child +can affect LLM performance [36], emphasizing the need to nodes from the root using a UCB-based utility function that +consider the order of the combination of chunks in RAG balances exploration and exploitation while incorporating cost +tasks. However, existing methods are not well-suited for cases control; (2) Expansion: generating all feasible child nodes; (3) +where optimal retrieval requires a specific chunk combination Simulation: performing parallel evaluations of the expanded +order, rather than isolated chunks. In this paper, we propose nodes via reranker scoring; and (4) Backpropagation: updat- +CARROT, a cost-constrained retrieval optimization framework ing path statistics with the obtained benefits. In particular, +that addresses the overlooked importance of considering the we leverage parallel reranker evaluations to process multiple +order of chunk combinations. chunk combinations simultaneously, substantially improving + 3) Reinforcement Learning for Large Language Models: computational efficiency. +Recently, reinforcement learning (RL) has been increasingly 2. Configuration Inference: A naive approach to config- +utilized in various data management and RAG tasks. The RL uration tuning is to exhaustively evaluate all configurations +technique can enable large language models to improve their and rerankers in parallel, which is prohibitively expensive for +generation ability by leveraging external knowledge sources, RAG systems. To address this, we introduce a Configuration +such as search engines [30]. In particular, the human feed- Agent that dynamically generates optimal settings—such as +back [15], [51] can be integrated to help models produce iteration count, cost coefficient, and exploration factor—based +more accurate and contextually relevant responses through on the query domain and retrieved data. Specifically, the +the RL framework. In addition, some query optimization configuration agent takes both the query and the retrieved +approaches [1], [8], [37] further refine retrieval processes, chunks as input, fusing their embeddings through a data- +allowing model performance to inform query adjustments and aware mechanism to capture the correlation between the query +ultimately enhance downstream task outcomes. In this work, intent and the actual data distribution, enabling configuration +we apply a lightweight RL technique MCTS to optimize the prediction conditioned on both query semantics and data +chunk combination order search process in the RAG system. characteristics. To enhance the model’s effectiveness, we em- +We also introduce a configuration agent to guide the MCTS ploy a contrastive learning approach that uses positive and +search process. To the best of our knowledge, this is the first negative label pairs: positive labels correspond to query-data +approach to addressing this problem. pairs with the same optimal reranker, while negative labels + come from different optimal rerankers. A joint loss function + IV. S YSTEM OVERVIEW is used to simultaneously optimize both the regression (for + Existing RAG frameworks face three major challenges: cap- parameter tuning) and contrastive learning (to enhance label +turing inter-chunk correlations, handling the non-monotonic differentiation). +utility of chunk order, and adapting to diverse query domains. RAG Pipeline. Our framework pipeline is shown in Figure 2. + We first generate an embedding for the input query, which Φvi , whose corresponding cost and benefit are denoted as +is then used to retrieve potential chunks from the vector cost(Φvi ) and W (Φvi ), respectively, as defined in Sec. II. +database. Both the query and retrieved chunks are fed into the Therefore, the task described in Definition 2 can be refor- +configuration agent, which dynamically generates the optimal mulated as searching for the node within the cost budget +configuration by analyzing the query-data correlation through B that yields the highest benefit. Formally, this is expressed +data-aware embedding fusion. Using the optimal configuration, as:vˆi = arg maxvi ⊆T, cost(Φvi )≤B W (Φvi ). +we can search the policy tree to determine the optimal chunk + B. MCTS-Based Policy Tree Search +combination and order from the retrieved potential chunks. +Finally, this optimal chunk combination is used to construct Enumerating all possible nodes in the policy tree T is +the final prompt for the LLMs. infeasible due to the exponential growth of the search space. + To efficiently explore this space, we employ an MCTS-based + V. C HUNK C OMBINATION S EARCH approach that incrementally expands nodes to construct a +Motivation. The optimal chunk combination order selection partial tree TMCTS , from which a solution can be derived +problem presents two fundamental challenges. First, the search without full enumeration. In each iteration, the algorithm + selects nodes to expand, evaluates their potential, and uses +space P grows exponentially: + n Pn for nn! candidate chunks, there the evaluation results to guide the next round of search. To +exist k=1 P (n, k) = k=1 (n−k)! ordered combinations +(e.g., n = 10 yields over 9 million combinations), mak- incorporate cost constraints and improve efficiency, we design +ing exhaustive enumeration infeasible. Second, greedy strate- a cost-aware node utility function to guide node selection and +gies may fail due to the non-monotonic nature of chunk introduce a parallel evaluation mechanism to accelerate the +benefits. For example, while individual chunks may satisfy scoring process. +W (χ1 ) > W (χ2 ), their chunk combination orders could yield Cost-aware Node Utility U(v). Because exhaustive explo- +W (⟨χ2 , χ3 ⟩) ≫ W (⟨χ1 , χ3 ⟩) in a real RAG scenario, as ration is infeasible, we guide the search with a UCB-style [52] +established in Section II. Once the greedy approach selects heuristic that balances exploitation and exploration while +χ1 in the first step, it cannot backtrack to explore the superior accounting for cost. For each node v, the algorithm maintains +combination ⟨χ2 , χ3 ⟩, thus missing the globally optimal chunk the cumulative reward V (v) (aggregated over historical visits) +combination order. To address these challenges, we design a and the visit count N (v). We use Nparent(v) to denote the visit +policy tree to organize the combination space and formulate count of v’s parent node. We define the cost-aware utility as +the chunk combination order selection task as an optimal node + s + V (v) ln Nparent(v) cost(Φv ) +search problem in the policy tree. We employ an MCTS-based U(v) = + c − λ , (2) + N (v) N (v) B +search algorithm that strategically explores only a small subset +of promising paths instead of enumerating all possibilities. The where c and λ are tuning parameters. This extends standard +key insight is that MCTS iteratively expands and evaluates UCB to our budget-constrained setting. +nodes along high-potential paths, accumulating node benefits Algorithm Overview. As outlined in Algorithm 1, we ini- +through backpropagation, and ultimately returns the node tialize the root node of the policy tree and iteratively refine +with the highest reranker-assigned benefit among the explored the tree until the iteration budget is exhausted. Each iteration +paths. Unlike greedy selection, MCTS’s exploration mecha- performs four phases: (1) Selection, traversing from root to +nism ensures that nodes with initially lower individual benefits an unexpanded node using node utility U(v); (2) Expansion, +can still be discovered if they lead to superior combinations. generating all possible child nodes within the cost budget; + (3) Simulation, evaluating all children in parallel and assign- +A. Policy Tree Search Modeling ing them node benefits W (Φv ); and (4) Backpropagation, +Policy Tree. As illustrated in Figure 3, we construct a policy updating the cumulative reward V (·) and visit count N (·) +tree to represent all possible orders of chunk combinations along the path to the root using the obtained benefit W (Φv ). +retrieved from the vector database. The root node corresponds After completing all iterations, we obtain the MCTS policy +to the initial state with no selected chunks, while each child tree TM CT S and select the node with the maximum reranker- +node is generated by appending one additional chunk to the or- assigned benefit W (Φv ) among all non-root nodes that satisfy +dered sequence represented by its parent node. Thus, each non- the cost budget constraint. Notably, unlike traditional MCTS, +root node corresponds to an ordered combination of chunks, which focuses on leaf nodes, our method considers nodes at all +incrementally built through this process. For example, if a node depths, as shorter combinations may outperform longer ones +represents the combination Φv1 = ⟨χ1 ⟩, its child nodes may due to budget constraints or semantic coherence. +correspond to ordered extensions such as Φv2 = ⟨χ1 , χ2 ⟩, Detailed Phases. We now describe the five phases of the +Φv3 = ⟨χ1 , χ3 ⟩, or Φv4 = ⟨χ1 , χ4 ⟩. MCTS-based policy tree search. +Remark. The entire policy tree does not need to be constructed (1) Selection (Function 1). Starting from the root, the algo- +and typically only a small portion of it is dynamically ex- rithm recursively selects child nodes guided by the utility +panded during our MCTS-based search process. function U(v) (Eq. 2), which balances exploitation, explo- +Optimal Node Search Modeling. Each non-root node vi ration, and cost. At each step, among available children, we +in the policy tree T represents a chunk combination order select the one with the highest U(v) value. The exploration + W=0.3 + R V=0.3 + χ1+χ2 N=1 + Root + R R + χ1 W=0.2 + χ W=0.1 … W=0.2 χ1+χ3 V=0.2 + …2 χ4 N=1 0.1 … W=0.2 + χ1 … χ4 W=0.1 … W=0.2 V=1.4 + V=0.1 V=0.2 N=4 + N=1 N=1 + χ1+χ2 χ1+χ3 χ1+χ4 χ1+χ4 Parallel W=0.7 + V=0.7 + Evaluate N=1 W =0.3 W =0.2 W =0.7 + + + Potential Policy Tree Selection Backpropagation Find Best + Chunks Expansion & Simulation Node + + + Fig. 3: Workflow of MCTS-Based Policy Tree Search + Algorithm 1: MCTS-Based Policy Tree Search Function 1: Selection + Input: q: a query; D: a set of candidate chunks; B: total Input: vi : current tree node; B: total budget + budget; Reranker: predicted reranker 1 while vi .isFullyExpanded() do +1 Initialize root node v0 with query q for the policy tree 2 foreach child nodes vj of vi do + TM CT S ; 3 compute utility +2 while within iteration do + r + V (v ) ln Nparent(v ) cost(Φvj ) +3 vselected = Selection(v0 , B); U(vj ) = N (vjj ) + c N (vj ) + j + −λ B + ; +4 {v1 , . . . , vk } = Expansion(vselected , D, B); +5 Simulation(vselected , {v1 , . . . , vk }, Reranker); 4 vi = arg maxvj ∈Children(vi ) U(vj ); +6 BackPropagation({v1 , . . . , vk }); 5 return vi +7 return arg maxv∈TM CT S ,cost(Φv )≤B W (Φv ) + Function 2: Expansion + Input: v: unexpanded node to expand; D: set of candidate + p chunks; B: total budget +term c ln Nparent(v) /N (v) in U(v) ensures that less-visited // Create all possible child nodes + 1 Expand v by using the candidate chunks in D to construct +nodes receive priority, preventing premature convergence to + child nodes {v1 , v2 , . . . , vk } whose costs do not exceed the +locally optimal paths. Selection proceeds until reaching an budget B; +unexpanded node, ensuring both deep and broad exploration 2 foreach child nodes vj do +of the search space. 3 calculate and record cost(Φvj ); +(2) Expansion (Function 2). Upon reaching an unexpanded 4 initialize N (vj ) = 0, V (vj ) = 0; +node v, all potential child nodes {v1 , v2 , . . . , vk } are generated 5 return {v1 , v2 , . . . , vk } +by appending one additional chunk (each child cost does Function 3: Simulation +not exceed the budget B) to the current combination. Unlike Input: v: parent node; {v1 , . . . , vk }: expanded children; +standard MCTS, which expands a single child per iteration, Reranker: predicted reranker +our approach expands all possible children simultaneously to // Parallel evaluation via reranker +prepare for parallel evaluation. In particular, we initialize each 1 compute benefit + +new node’s cumulative reward and visit count to zero. [W (Φv1 ), . . . , W (Φvk )] = Reranker(Q, [Φv1 , . . . , Φvk ]); + 2 foreach child nodes vj do +(3) Simulation: Parallel Evaluation (Function 3). Instead of 3 record W (Φvj ); +random rollouts, we evaluate all expanded nodes in +parallel using the reranker [W (Φv1 ), . . . , W (Φvk )] = Function 4: BackPropagation +Reranker(Q, [Φv1 , . . . , Φvk ]), where W (Φvj ) denotes the Input: {v1 , v2 , . . . , vk }: the expanded child nodes +reranker-assigned benefit for combination Φvj . This design // Backpropagate each child +allows all k combinations to be scored in one model call (cost 1 foreach child nodes vj do + +O(r)) rather than k separate calls (cost O(k · r)), leveraging 2 vcurrent = vj ; + 3 repeat +transformer rerankers’ batch processing for substantial effi- 4 V (vcurrent ) = V (vcurrent ) + W (Φvj ); +ciency gains. For each vj , we record W (Φvj ) and compute its 5 N (vcurrent ) = N (vcurrent ) + 1; +token cost cost(Φvj ). 6 vcurrent = Parent(vcurrent ); +(4) Backpropagation (Function 4). Each child’s benefit 7 until vcurrent is None; +W (Φvj ) is propagated upward along the path to the root, +updating cumulative rewards V (·) and visit counts N (·). +Unlike traditional MCTS, which updates only the best path, +we backpropagate all children independently, preserving assigned benefit W (Φv ) rather than the averaged reward +richer exploration statistics to guide future iterations. V (v)/N (v), as W (Φv ) more accurately reflects the true +(5) Final Node Selection. After all iterations, we perform relevance between query and chunk combination order. When +a global search across explored nodes and select the multiple nodes have identical W (Φv ), we prefer nodes with +one with the highest benefit under the cost budget: more visits and greater depth. +v̂ = arg maxv∈TM CT S , cost(Φv )≤B W (Φv ). Unlike traditional Complexity Analysis. Let N be the number of candidate +MCTS methods, the final decision is based on the reranker- chunks, B the token budget, s the fixed chunk token cost, + Training pair + Query1: “What is the capital city of Ground Truth + Feature + Germany?” + Relevance Chunks LConstrastive Query1 + Map Pair + Query2: “Which country is Berlin‘s Best reranker:jina-reranker-v1-turbo-en + capital?” + Relevance Chunks + Best parameters + Label: Positive pair (Iteration = 30, 𝜆= 0.1, C = 2.4) + Reranker + LClassification Query2 + Class + Query3: " How to bake a Classification Best reranker:jina-reranker-v1-turbo-en + chocolate cake?” + Relevance Encoding Network + Best parameters + Chunks Iteration, (Iteration = 24, 𝜆= 0.2, C = 2.6) + LRegression + Query4: “What is the meaning of λ,C + happiness?” + Relevance Input + Regression + Chunks Embeddings + Feature Map + Joint Training + Label: Negative pair + + Fig. 4: Overview of Configuration Agent + + WikiPassageQA MARCO + 1.0 1.0 (i) the N ! combinatorial space makes supervised data col- + 0.9 0.9 + Best Reranker Proportion + + + + + 0.8 0.8 + lection infeasible; (ii) non-monotonic utility functions where + 0.7 0.7 W (A ∪ B) ̸= W (A) + W (B) are difficult for neural networks + 0.6 0.6 + 0.5 0.5 to approximate; (iii) predicting chunk combination orders re- + 0.4 0.4 + 0.3 0.3 + quires structured outputs from the N ! space, whereas predict- + 0.2 0.2 ing MCTS hyperparameters reduces to lightweight regression. + 0.1 0.1 + 0.0 Academic Commerce Literature Healthcare Legal Others 0.0 Academic Commerce Literature Healthcare Legal Others + Accordingly, we present the agent framework in Section VI-A + Domain Domain + jina-v1-turbo bge-v2-m3 bge-base and the learning pipeline in Section VI-B. + jina-v2-multi bge-large gte-base + A. Model Framework + Fig. 5: Best Reranker Distribution Design Rationale. A straightforward approach is to use a + multilayer perceptron (MLP) classifier to predict the optimal +and r the maximum cost of a single batched reranker call. reranker for each query. However, due to the high semantic +Any budget-feasible combination contains at most DB = and domain diversity of RAG queries, such classifiers of- +min{N, ⌊B/s⌋} chunks. ten perform poorly, as shown in our experimental result in +(1) Time Complexity: Each of the I iterations performs selec- Table VI. To overcome this limitation, we adopt a Siamese +tion, expansion, simulation, and backpropagation. (i) Selection network trained with contrastive learning, which encourages +traverses O(DB ) levels with O(N ) children per level, yielding similar query representations to be closer while pushing apart +O(DB · N ) complexity; (ii) Expansion creates up to N dissimilar ones, promoting cross-domain generalization [53]. +children, contributing O(N ); (iii) Simulation evaluates all ex- Figure 4 illustrates the overall architecture, which comprises +panded children in a single batched reranker call, costing O(r); two main modules for transforming the input into feature +(iv) Backpropagation updates O(N ) children across O(DB ) maps. First, the embedding module generates representations +levels, giving O(DB · N ). Thus, the total time complexity is for input queries and their retrieved chunks. Then, the encod- +O(I · (DB · N + r)). A smaller B reduces DB , shrinking the ing network refines these embeddings into feature maps, which +search space. In practice, I is predicted by the configuration are used by classification or regression layers to produce the +agent. As shown in Fig. 9, I remains a small value, ensuring final configuration outputs. The following sections describe +efficient search. each component in detail. +(2) Space Complexity: Space is allocated to store the nodes of (1) Input Embedding. Given a query and its retrieved chunks + PI where ki ≤ +the tree. In the i-th iteration, we expand ki nodes, from the vector database, we employ the BGE-M3 model [54] +N . Over I iterations, this results in a total of i=1 ki ≤ I · N to generate 1024-dimensional embeddings for the query and +nodes, leading to a space complexity of O(I · N ). the mean-pooled representation of retrieved chunks. Encoding + the retrieved chunks enables the agent to capture query-related + VI. C ONFIGURATION AGENT data characteristics, enhancing its sensitivity to contextual +Motivation. The MCTS process requires critical configura- nuances. These embeddings preserve semantic information, fa- +tions including reranker selection, iteration count, exploration cilitating efficient comparison and classification by the encod- +coefficient, and cost coefficient. Our preliminary experiments ing network. This design improves retrieval relevance across +(details in our technical report [40]) reveal that optimal diverse query types by explicitly modeling the relationship +configurations vary across queries and retrieved data. For between query intent and data distribution. +instance, Fig. 5 shows that different query domains (Academic, (2) Feature Map Generation with Encoding Network. To op- +Commerce, etc.) exhibit distinct best-performing rerankers, timize reranker selection across diverse query types, we +validating the necessity of adaptive configuration. Therefore, employ an encoding network that jointly learns classifica- +we design a configuration agent that predicts optimal MCTS tion and configuration representations. A Siamese network +configurations based on both query and chunk characteristics. with three fully connected layers fuses query and chunk +A natural alternative is to directly predict chunk combination embeddings, capturing query–data correlations for context- +orders via a learned model. However, this is impractical: dependent predictions. Both branches share weights with linear + layers and ReLU activations, reducing the concatenated 2048- Algorithm 6: Joint Contrastive Training +dimensional input (x ⊕ c) through layers of 512, 256, and Input: Query embedding pairs (xi , xj ), chunk embeddings +128 dimensions. The output layer produces (i) a classification (ci , cj ), labels (yi , yj ), params (pi , pj ), similarity +result identifying the optimal reranker and (ii) regression out- indicator Iij (0 for same class, 1 for different class) +puts predicting MCTS configurations (iterations, exploration Output: models fθ , gθ and hθ + 1 Initialize fθ with projection head; +coefficient c, and cost coefficient λ). By conditioning on 2 for epoch = 1 to N do +both query and chunk embeddings, this architecture adaptively 3 for batch (xi , xj , ci , cj , Iij , yi , yj , pi , pj ) do +controls various search parameters. For example, by predicting 4 zi ← fθ (xi ⊕ ci ), zj ← fθ (xj ⊕ cj ) +iterations, it adjusts search depth: fewer iterations for simple yipred ← gθ (zi ), ppred i ← hθ (zi ); +query-data pairs where lightweight search suffices, more for 5 + pred + yj ← gθ (zj ), ppred + j ← hθ (zj ); +complex ones requiring deeper exploration. 6 Lcon ← +B. Joint Training (1 − Iij )∥zi − zj ∥2 + Iij max(0, γ − ∥zi − zj ∥)2 + Lcla ← CE(yipred , yi ) + CE(yjpred , yj ); // CE + In this section, we present the training pipeline for the is the cross-entropy loss +configuration agent (Figure 4). It integrates three joint tasks: 7 Lreg ← ∥ppred − pi ∥2 + ∥ppred − p j ∥2 ; + i j +classification for reranker selection, regression for MCTS 8 Update θ using ∇θ (Lcon + Lcla + Lreg ); +hyperparameter prediction, and contrastive learning to further +enhance representation quality. + 1) Classification and regression losses: Let the query and +its relevant chunk embeddings be denoted as x and c, re- +spectively, where ⊕ denotes the concatenation operation. The with different rerankers), we first generate their corresponding +encoding network is represented by fθ , with a classification feature maps with the encoding model fθ . These feature maps +head gθ and a regression head hθ in the output layer. The clas- are then utilized to compute the contrastive loss Lcon . As +  +sification loss is defined as Lcla (θ) = Fcla gθ (fθ (x⊕c)), ytrue , shown in Algorithm 6, the contrastive loss uses the similarity +where ytrue is the ground-truth optimal reranker obtained indicator Iij to handle both positive and negative pairs in a +through actual reranker execution, and Fcla denotes the cross- unified formulation. This loss function is designed to ensure +entropy loss between predicted and true labels. Similarly, the that query-data pairs with the same reranker are brought closer +regression loss is given by Lreg (θ) = Freg hθ (fθ (x⊕c)), ptrue , +  together in the embedding space, while those with different +where ptrue represents the optimal MCTS hyperparameters rerankers are distanced. +(iterations, exploration coefficient c, and cost coefficient λ) 3) Whole training process: As shown in Algorithm 6, we +identified via grid search on real query executions, and Freg implement the complete training process by designing a loss +denotes the mean squared error (MSE) loss. function Ltotal that combines contrastive, classification, and + 2) Contrastive Learning: To efficiently distinguish between regression losses as follows: Ltotal (θ) = Lcon (θ) + Lcla (θ) + +different query domains and recommend the optimal configu- Lreg (θ). In particular, the contrastive loss Lcon (θ) encourages +ration for each query, we utilize contrastive learning to bring the embeddings of queries with the same optimal reranker +queries with the same optimal reranker closer together while to be close together, while pushing apart the embeddings of +pushing apart embeddings from different reranker classes. queries with different rerankers. The classification loss Lcla (θ) +Contrastive pairs preparation. First, we obtain ground truth aids the model in correctly identifying the reranker using +labels via grid search over parameters using ROUGE-L, cross-entropy, and the regression loss Lreg (θ) minimizes the +evaluating different parameter combinations to select con- error in predicting the optimal MCTS configuration. +figurations yielding optimal response quality. Next, we gen- Complexity Analysis We analyze the computational complex- +erate contrastive pairs based on these optimal annotations: ity of both training and inference procedures for the Configura- +positive pairs share the same optimal reranker (promoting tion Agent. (1) During offline training, we adopt a contrastive +minimal embedding distance), and negative pairs use different learning framework that processes N training pairs in each +rerankers (maximizing embedding distance). We construct batch. The computational cost of generating embeddings is +approximately 10,000 training pairs. The labeling process only O(N · TE ), where TE denotes the embedding generation time +requires pairwise preference comparisons between configura- for a single sample. Subsequently, each embedding is passed +tions, and we deploy Llama3-8B locally to generate training through our lightweight configuration network, incurring an +pairs. Since the contrastive pairs are constructed from query- additional cost of O(N ·TS ), where TS represents the process- +chunk embeddings with ground truth labels, the agent learns ing time per sample in this smaller network. Since the con- +query-context patterns that share similar optimal configura- figuration network is significantly smaller and faster than the +tions. These patterns become domain-invariant representations, embedding network (e.g., BGE-M3), we have TS ≪ TE . Thus, +enabling generalization to different downstream LLMs and the total training time complexity is dominated by embedding +unseen domains without retraining. generation: O(N · TE + N · TS ) = O(N · TE ). This implies +Contrastive loss. As illustrated in Figure 4, for a given pair that the training complexity scales linearly with the number of +(xi ⊕ ci , xj ⊕ cj ) with similarity indicator Iij (Iij = 0 for training samples. (2) For online inference, suppose the query +positive pairs with the same reranker, Iij = 1 for negative pairs workload size is |Q| and the network latency per query is + TABLE I: Statistics of datasets used in the experiments. GPUs for 16 hours and 4 A100 GPUs for 12 hours, re- + Dataset #train #dev #test #p spectively. We therefore include ColBERT as a representative + baseline for fairness, noting that such tuning-based methods + MARCO 808,731 101,093 101,092 8,841,823 + WikiPassageQA 3,332 417 416 244,136 are often impractical for large-scale deployment due to their + HotpotQA 90,564 7,405 14,810 5,000,000+ prohibitive training and indexing costs. Details of all baselines + are provided in our technical report [40]. +C. The overall inference complexity is then O(C · |Q|). In + In addition, we remove the configuration agent from our +our empirical study in Table VI, both classification prediction + method as a baseline to evaluate its impact on the performance +and parameter prediction process queries within an acceptable + of CARROT, referring to this version as CARROT w/o Agent. +efficiency range. + Finally, we implement a method called CARROT Upper to + VII. E XPERIMENT establish an upper bound by enumerating all possible chunk + The experimental study intends to answer the following combinations within the top-5 chunk candidates and selecting +questions: the optimal chunk combination order. We adopt Llama3- +• RQ1: (1) How effective is our CARROT for the cost- 8B [61] as the default large language model for these methods. + constrained RAG pipeline compared to other methods? (2) Hyper-parameter Settings. The hyper-parameters for CAR- + Does CARROT consistently maintain its effectiveness across ROT are automatically determined by the configuration agent, + different LLMs? while NaiveRAG does not require hyper-parameters. For other +• RQ2: How efficient is CARROT compared to different baseline methods, we ensure consistency by using identical + baselines? hyper-parameters for fair comparisons. Specifically, we set the +• RQ3: How does CARROT perform with large-scale datasets exploration coefficient to 2.4, the number of iterations to 10, + when evaluating its scalability? and the cost coefficient λ to 0.1. Due to the computational +• RQ4: How does the performance of CARROT vary across constraints imposed by combinatorial complexity, we set the + different cost budget constraints? candidate chunks to 5 in the CARROT Upper case. By default, +• RQ5: What is the effectiveness of each component in we set the fixed chunk size to 256 and the budget to 1024 for + CARROT? fair ablation studies. For offline data collection, we sample 1K + seed queries from three datasets and perform grid search over +A. Experimental Setting 6 rerankers with 20 parameter combinations per query. +Environment. We integrate our framework with the RAG Learning Setting. In our method, the configuration agent +framework LlamaIndex [32] and Faiss [55]. Experiments used is trained using contrastive learning. The hyper-parameters +dual Intel Xeon Gold 6326 CPUs (32 cores, 64 threads), used during this process include a margin for contrastive loss +512 GB DDR4 RAM, and 960 GB NVMe SSD. The con- (margin=1.0), learning rate (lr=0.001), batch size (32), number +figuration agent is implemented in PyTorch 2.4, with data of epochs (num epochs=60), and the embedding model (i.e., +collection and training performed on 8×A5000 GPUs. We BAAI/bge-m3 [54]). +choose OpenRouter [56] to deploy our inference service. Evaluation Metrics. Following [12], [14], [15], we utilize +Datasets. To evaluate the performance of CARROT across di- ROUGE-1, ROUGE-L [62], and BLEU-1 [63] for single- +verse scenarios, we conduct experiments on three datasets with hop retrieval tasks, and F1 score [64] for multi-hop retrieval +differing task focuses (Table I): (1) WikiPassageQA [57] is a scenarios. To evaluate efficiency, we measure the retrieval +question-answering benchmark containing 4,165 questions and latency across different methods. +over 100,000 text chunks, designed for non-factoid question +answering and passage retrieval. (2) MARCO [58] is a compre- B. Performance Comparison +hensive dataset tailored for natural language processing tasks, 1) RQ1-(1): Effectiveness Comparison Across Datasets and +primarily emphasizing question answering and retrieval. (3) Chunk Sizes: As shown in Table II, we evaluate the ef- +HotpotQA [59] is a question-answering benchmark designed fectiveness of CARROT compared to various baselines on +for multi-hop reasoning tasks, consisting of approximately three datasets: WikiPassageQA, MARCO, and HotpotQA. The +113K QA pairs sourced from Wikipedia. first two are single-hop datasets, while the last is a multi- +Baselines. We compare CARROT against representative rank- hop dataset. For methods that support token-level budget +based RAG methods, including RAPTOR [6], NaiveRAG [28], constraints (e.g., RAPTOR, NaiveRAG, and HYDE), we fix +HYDE [29], and FLARE [17], to demonstrate improvements the token budget to 1024. For other methods, we report results +in retrieval relevance and response quality. We also evaluate without budget limitations. To ensure fairness, all methods are +graph-based approaches such as GraphRAG [13], Graph- built upon Llama3-8B. The experimental results lead to the +CoT [25], and G-Retriever [60], highlighting our efficiency following observations: +advantages. Finally, we include comparisons with tuning-based (1) CARROT consistently outperforms all baselines across +methods, despite their substantial computational overhead and different datasets. Specifically, it achieves about 30% relative +infrastructure requirements. For instance, ColBERT [23] re- gain on both single-hop and multi-hop tasks. As expected, it +quires 3 hours to index 9M passages on four V100 GPUs, does not surpass the CARROT upper, which represents a nearly +while RankRAG [14] and Self-RAG [21] demand 128 A100 exhaustive search over chunk combinations. + TABLE II: Effectiveness Comparison and Token Cost Across Datasets (All methods are built upon Llama3-8B. Best in bold, + second-best underlined). On.=Average online token cost (per-query). + WikiPassageQA MARCO HotpotQA + Method 256 512 1024 On.↓ 256 512 1024 On.↓ 256 512 1024 On.↓ + R1 RL B-1 R1 RL B-1 R1 RL B-1 R1 RL B-1 R1 RL B-1 R1 RL B-1 F1 F1 F1 + RAPTOR 0.328 0.306 0.345 0.322 0.301 0.333 0.335 0.305 0.370 1024 0.386 0.356 0.371 0.393 0.366 0.370 0.338 0.316 0.328 1024 0.248 0.252 0.245 1024 + NaiveRAG 0.327 0.302 0.333 0.321 0.297 0.321 0.334 0.303 0.338 1024 0.398 0.369 0.385 0.395 0.368 0.372 0.337 0.312 0.324 1024 0.243 0.248 0.240 1024 + HYDE 0.311 0.301 0.323 0.306 0.286 0.311 0.318 0.290 0.328 1024 0.367 0.338 0.352 0.373 0.348 0.349 0.321 0.300 0.312 1024 0.228 0.235 0.225 1024 + ColBERT 0.329 0.304 0.335 0.324 0.300 0.326 0.333 0.307 0.336 1024 0.376 0.357 0.358 0.375 0.349 0.351 0.324 0.309 0.319 1024 0.247 0.253 0.244 1024 + Graph-CoT 0.362 0.343 0.324 0.359 0.341 0.321 0.364 0.345 0.326 14237 0.364 0.345 0.349 0.362 0.343 0.347 0.360 0.342 0.345 14576 0.294 0.297 0.295 14011 + G-Retriever 0.356 0.337 0.318 0.353 0.335 0.315 0.358 0.339 0.320 13239 0.358 0.339 0.343 0.356 0.337 0.341 0.354 0.336 0.339 12428 0.281 0.284 0.282 13003 + FLARE 0.367 0.348 0.330 0.364 0.346 0.327 0.370 0.351 0.333 16913 0.370 0.351 0.355 0.368 0.349 0.353 0.366 0.348 0.352 16584 0.305 0.307 0.305 15022 + GraphRAG 0.337 0.315 0.311 0.322 0.302 0.291 0.331 0.290 0.304 1024 0.376 0.343 0.357 0.370 0.342 0.351 0.307 0.295 0.304 1004 0.298 0.318 0.291 1001 + HippoRAG2 0.363 0.345 0.328 0.360 0.343 0.325 0.367 0.348 0.331 1024 0.367 0.348 0.352 0.365 0.347 0.350 0.363 0.346 0.349 1003 0.311 0.327 0.313 1028 + CARROT upper 0.437 0.416 0.395 0.426 0.406 0.423 0.444 0.423 0.420 923 0.435 0.414 0.431 0.425 0.397 0.411 0.447 0.426 0.420 822 0.347 0.357 0.339 1011 + CARROT w/o Agent 0.390 0.364 0.337 0.372 0.347 0.379 0.388 0.362 0.368 838 0.401 0.374 0.389 0.393 0.372 0.377 0.393 0.364 0.368 725 0.299 0.312 0.307 1021 + CARROT 0.432 0.401 0.392 0.413 0.381 0.394 0.419 0.386 0.397 811 0.425 0.391 0.413 0.419 0.386 0.400 0.423 0.393 0.405 789 0.320 0.349 0.336 1007 + + TABLE III: Effectiveness Comparison Across Different LLMs (best in bold, second-best underlined) + Dataset LLM RAPTOR NaiveRAG HYDE ColBERT Graph-CoT G-Retriever FLARE GraphRAG HippoRAG2 CARROT upper CARROT w/o Agent CARROT + WikiPassageQA Llama3-8B 0.328 0.327 0.311 0.329 0.362 0.356 0.367 0.337 0.363 0.437 0.390 0.432 + DeepseekV3 0.348 0.347 0.330 0.348 0.371 0.368 0.389 0.358 0.369 0.463 0.413 0.459 + (Rouge-1 score ↑) GPT-4o 0.341 0.340 0.324 0.344 0.367 0.371 0.382 0.351 0.361 0.454 0.406 0.449 + MARCO Llama3-8B 0.386 0.398 0.367 0.376 0.364 0.358 0.370 0.376 0.367 0.435 0.401 0.425 + DeepseekV3 0.409 0.422 0.389 0.379 0.419 0.428 0.429 0.399 0.410 0.461 0.425 0.454 + (Rouge-1 score ↑) GPT-4o 0.401 0.414 0.382 0.372 0.411 0.418 0.422 0.391 0.402 0.452 0.417 0.440 + HotpotQA Llama3-8B 0.248 0.243 0.228 0.247 0.294 0.281 0.305 0.298 0.311 0.347 0.299 0.320 + DeepseekV3 0.263 0.258 0.242 0.262 0.311 0.298 0.319 0.316 0.329 0.368 0.317 0.341 + (F1 score ↑) GPT-4o 0.259 0.254 0.238 0.256 0.305 0.291 0.311 0.307 0.318 0.355 0.309 0.331 + + GraphRAG HYDE + 90 HippoRAG2 + Graph-CoT + NaiveRAG + ColBERT 80 + GraphRAG TABLE IV: Cost Breakdown on WikiPassageQA (Llama3-8B). + FLARE CARROT w/o Agent + 80 G-Retriever CARROT HippoRAG2 + Raptor 70 Offline Indexing Online Quality + 70 Graph-CoT Method + 60 60 Time(s) Token Time(s) Token R1↑ RL↑ + Time Cost (s) +Latency (s) + + + + + 50 Raptor + 50 FLARE NaiveRAG 61.2 – 2.3 1024 0.327 0.302 + 40 40 HYDE 66.1 – 28.7 1024 0.311 0.301 + 30 30 ColBERT 1731 – 2.5 1024 0.329 0.304 + G-Retriever HYDE + 20 20 G-Retriever 1281.1 – 22.8 13239 0.356 0.337 + 10 FLARE 70.0 – 45.5 16913 0.367 0.348 + 10 + 0 NaiveRAG ColBERT CARROT + WikiPassageQA MARCO HotpotQA 0 0.26 0.28 0.30 0.32 0.34 0.36 0.38 0.40 RAPTOR 21301.0 0.9M 55.1 1024 0.328 0.306 + Datasets + RougeL Score Graph-CoT 28888.1 2.1M 61.3 14237 0.362 0.343 + GraphRAG 55003.2 2.3M 80.2 1024 0.337 0.315 + (a) Efficiency Comparison (b) Time Cost vs Response HippoRAG2 32401.3 1.7M 78.0 1024 0.363 0.345 + Across Datasets Quality Tradeoff CARROT 66.9 – 3.1 811 0.432 0.401 + + Fig. 6: Efficiency and Quality Trade-off Analysis 3) RQ2: Efficiency Evaluation: As depicted in Figure 6a, + we evaluate the retrieval efficiency by comparing the latency + (2) Optimal chunk sizes vary across methods and datasets. For + between different methods across the three datasets, with + instance, CARROT performs best with a chunk size of 256 + the chunk size set at 256. The results show that NaiveRAG + in the single-hop datasets and 512 in the multi-hop setting. + requires the least time for retrieval as it directly invokes + Meanwhile, the CARROT upper achieves its best results with + the reranker model and uses the vector index to calculate + a chunk size of 1024 in the single-hop dataset while 512 in + similarities. On the other hand, CARROT requires more time as + multi-hop dataset. Notably, the average number of tokens used + it employs a configuration agent to predict optimal parameters + in HotpotQA is higher than in single-hop datasets, likely due + and utilizes a policy tree to search for the optimal chunk + to the increased complexity of multi-hop reasoning, which + combination. Conversely, CARROT w/o Agent, which operates + benefits from larger chunk sizes that capture more context. + with default parameter settings, demonstrates reduced re- + (3) CARROT upper achieves better performance at a modest trieval time compared to CARROT. Additionally, other baseline + increase in cost. For example, on WikiPassageQA, the R1 methods, which involve invoking LLMs to process retrieval, + score improves from 0.432 to 0.437 while the average token exhibit higher latency. For instance, RAPTOR and Graph-based + usage increases from 811 to 923, demonstrating that exhaustive methods, which use an external LLM for summarization or + search with larger search spaces provides diminishing returns, extraction, showed significantly reduced efficiency. + validating the cost-quality trade-off of CARROT. + Figure 6b demonstrates that CARROT achieves high re- + 2) RQ1-(2): Effectiveness Comparison Across LLMs: As sponse quality within acceptable time costs. The comparable + shown in Table III, we evaluated CARROT across three LLMs: latencies between NaiveRAG and CARROT result from key + Llama3-8B [61], DeepseekV3 [65], and GPT-4o [66]. The optimizations: (1) Minimal LLM calls: CARROT invokes the + evaluation utilizes Rouge-1 for WikiPassageQA, MARCO and LLM only once at final inference after tree search completion. + F1 scores for HotpotQA, with a standardized cost constraint (2) Shallow search depth: optimal gold passages are typically + of 1024 tokens. The results indicate that CARROT consistently covered by several chunks, resulting in shallow tree depths. + outperforms baseline methods across all LLMs, with particu- (3) Parallel expansion: parallel mechanisms enable concurrent + larly strong performance when integrated with DeepseekV3, computation of multiple candidate nodes’ utility scores with- + highlighting our method’s generalizability. out significantly increasing external model calls. + TABLE V: Effectiveness comparison vs. budgets (best in bold, second-best underlined). On.=Online token cost (per-query). + Budget=1024 Budget=2048 Budget=8192 + Methods WikiPassageQA MARCO HotpotQA WikiPassageQA MARCO HotpotQA WikiPassageQA MARCO HotpotQA + R1↑ On.↓ R1↑ On.↓ F1↑ On.↓ R1↑ On.↓ R1↑ On.↓ F1↑ On.↓ R1↑ On.↓ R1↑ On.↓ F1↑ On.↓ + RAPTOR 0.328 1024 0.386 1024 0.248 1024 0.334 2048 0.389 2048 0.255 2048 0.330 8192 0.381 8192 0.247 8192 + NaiveRAG 0.327 1024 0.398 1024 0.243 1024 0.333 2048 0.401 2048 0.250 2048 0.328 8192 0.392 8192 0.242 8192 + HYDE 0.311 1024 0.367 1024 0.228 1024 0.317 2048 0.371 2048 0.236 2048 0.312 8192 0.363 8192 0.227 8192 + ColBERT 0.329 1024 0.376 1024 0.247 1024 0.335 2048 0.379 2048 0.254 2048 0.330 8192 0.371 8192 0.246 8192 + Graph-CoT 0.362 14156 0.364 14089 0.262 14823 0.365 14298 0.367 14312 0.269 15067 0.361 14421 0.363 14487 0.260 15213 + G-Retriever 0.356 13102 0.358 13187 0.270 13356 0.359 13245 0.361 13312 0.276 13489 0.354 13378 0.356 13456 0.268 13612 + FLARE 0.367 16789 0.370 16102 0.301 16867 0.370 16912 0.373 16278 0.309 17023 0.366 17045 0.369 16421 0.303 17189 + GraphRAG 0.337 1024 0.376 1004 0.298 1001 0.343 2048 0.381 2048 0.305 2048 0.335 8192 0.369 8192 0.292 8192 + HippoRAG2 0.363 1024 0.367 1003 0.311 1028 0.369 2048 0.372 2048 0.318 2048 0.362 8192 0.365 8192 0.309 8192 + CARROT w/o Agent 0.390 823 0.401 841 0.299 987 0.372 856 0.393 712 0.312 1018 0.388 879 0.393 748 0.307 1035 + CARROT upper 0.437 912 0.435 938 0.347 997 0.426 934 0.425 867 0.357 1012 0.444 971 0.447 835 0.339 1027 + CARROT 0.432 798 0.425 776 0.320 991 0.417 823 0.419 801 0.349 1008 0.420 847 0.420 819 0.336 1015 + + +TABLE VI: Ablation Study of Components (WikiPassageQA) consistently outperforms all baseline methods regardless of + Methods (Chunk Size=256) + Quality Online Cost budget constraints. Notably, CARROT consistently maintains + R1↑ RL↑ Tok. Time average token consumption below designated budget thresh- + Top-k+JinaV2 0.327 0.302 1024 2.3 olds. Furthermore, several baseline methods do not improve + w Agent-Only 0.297 0.271 795 2.5 + w Beam Search 0.380 0.354 820 3.0 monotonically with larger budgets, empirically validating our + w Bandit 0.362 0.337 815 2.9 observation in Challenge 2 that chunk utility does not follow a + w MLP 0.341 0.318 992 3.5 + w/o Agent 0.390 0.364 838 2.6 monotonic relationship with context length. In contrast, several + w/o Predicted Reranker 0.367 0.342 805 2.7 + w/o Predicted C 0.394 0.367 815 2.8 baseline approaches either exceed the allocated budgets or + w/o Predicted λ 0.391 0.364 812 2.8 consume them entirely, rendering such methods impractical + w/o Parallel Expansion 0.401 0.374 840 16.1 + for real-world deployment scenarios. + CARROT 0.432 0.401 811 3.1† +† Remark. For adaptable baselines like NaiveRAG and HYDE, + Latency breakdown: Embed 0.2s (6.5%), Retrieval 0.5s (16.1%), Config Agent 0.5s +(16.1%), Simulation 1.5s (48.4%), Tree Ops 0.3s (9.7%), Other 0.1s (3.2%). we adapted these methods by limiting their token, stopping the + addition of chunks when the output would exceed constraints. + Table IV decomposes computational costs into two dimen- For graph-based methods such as GraphRAG and HippoRAG2, +sions: Offline Indexing and Online retrieval. Offline Indexing costs shown here reflect online token cost only; offline graph +captures per-corpus preprocessing costs that must be repeated construction costs are discussed separately in Table IV. +for each new corpus, e.g., GraphRAG requires 2.3M tokens +for WikiPassageQA indexing. CARROT requires negligible C. RQ5: Ablation Study +per-corpus indexing of 66.9s. For online retrieval, CARROT 1) Ablation Studies on Different Components.: Table VI +achieves 811 tokens per query with R1=0.432, demonstrat- demonstrates that each component contributes substantially: +ing superior token efficiency compared to baselines, making removing the policy tree, configuration agent, or predicted +CARROT suitable for large-scale deployments. parameters all cause significant performance decline. Notably, +Remark (One-time Data Collection and Pre-training Cost). the parallel expansion mechanism enhances efficiency by +CARROT requires 1.8 hours for data collection and about 7 reducing latency from 16.1s to 3.1s (80.7% reduction). The +minutes for agent training, which is acceptable compared to latency breakdown (Table VI footnote) reveals that the Simu- +training-based methods such as ColBERT (10+ hours) and G- lation phase requiring reranker inference dominates (48.4%), +Retriever (6.2 hours). This is a one-time cost and can be reused while MCTS Tree Ops add only 9.7% overhead, confirming +across deployments, as the configuration agent generalizes to that MCTS introduces acceptable computational cost for its +unseen domains and different LLMs (Figure 11). quality gains. To further justify MCTS necessity, we compare + 4) RQ3: Scalability Evaluation: To evaluate the scala- five search strategies: (1) Top-k+JinaV2 (k=10) applies a fixed +bility of CARROT, we use the large-scale WikiPassageQA reranker to top-k candidates without exploring chunk combi- +dataset and vary the number of chunks to assess performance nation orders, achieving R1=0.327; (2) Agent-Only extends +under increasing data volumes. As shown in Figure 7, we the configuration agent to directly predict chunk combination +randomly sample between 10K and 100K chunks from the order scores by adding a scoring head, bypassing MCTS +full dataset and measure the retrieval latency across different search; it achieves the lowest quality because direct prediction +baselines. Despite the tenfold increase in data size, CAR- cannot effectively consider chunk correlations; (3) Bandit [52] +ROT’s latency rises by only about 10%, demonstrating strong employs arm-level exploration but neglects chunk combi- +scalability in handling large-scale retrieval tasks. This stems nation order dependencies, achieving R1=0.362; (4) Beam +from CARROT’s ability to effectively prune the search space, Search [67] maintains top-k candidates per step yet remains +maintaining a favorable balance between performance and limited by greedy local exploration, achieving R1=0.380; (5) +computational overhead. In contrast, graph-based and online CARROT with MCTS systematically explores chunk order de- +reasoning methods exhibit significantly higher overhead. pendencies via tree-based global search, achieving R1=0.432, + 5) RQ4: Effectiveness Evaluation on Different Budgets: a 32.1% gain over Top-k+JinaV2 with minimal overhead. +As demonstrated in Table V, we evaluate CARROT under 2) Ablation Studies on Design Choices.: We conduct three +various budget constraints to assess its performance. Ex- ablation studies on key factors affecting RAG performance. +periments conducted across the three datasets with budget (1) Validity of Reranker Scores as Simulation Reward. +limits of 1024, 2048, and 8192 tokens reveal that CARROT In CARROT’s policy tree search, the reranker assigns node + 80 + 70 40 + 0.65 + + + + + Iterations / External Model Calls + 0.450 + 60 + 0.60 + r = 0.83 35 + Latency (s) + + + 50 0.425 30 + 0.55 + + + + + RougeL Score + 40 0.400 25 + 0.50 + 30 + + + + + ROUGE-L + 0.375 20 + 20 0.45 + 0.350 15 + 10 0.40 + 0.325 10 + 0 0.35 + 5 + 0.300 + 0.30 + k + + + k + + + k + + + k + + + k + + + k + + + k + + + k + + + k + + + 0k + 10 + + + 20 + + + 30 + + + 40 + + + 50 + + + 60 + + + 70 + + + 80 + + + 90 + + + 10 + 10 20 30 40 50 60 10 20 30 40 50 60 + Number of Chunks 0.25 Chunk Candidates Chunk Candidates + GraphRAG G-Retriever NaiveRAG HippoRAG2 0.200.3 + Graph-CoT Raptor CARROT ColBERT 0.4 0.5 0.6 0.7 0.8 0.9 1.0 CARROT Graph-CoT FLARE Raptor NaiveRAG + FLARE HYDE Reranker Score (Normalized) GraphRAG ColBERT G-Retriever HYDE HippoRAG2 + + + + Fig. 7: Scalability Comparison (WikiPassageQA) Fig. 8: Reranker-Quality correlation Fig. 9: Quality/Efficiency vs. Candidates + + Llama3-8B 0.50 0.44 + Single-hop + 0.45 DeepseekV3 Multi-hop Academic 0.409 0.389 0.377 0.391 0.368 0.382 WikiPassageQA 0.398 0.371 0.324 + 0.42 + GPT-4o 0.45 + 0.40 + + + + + Train Dataset + 0.41 + Commerce 0.386 0.412 0.378 0.371 0.376 0.389 + 0.40 0.40 +ROUGE-L + + + + + ROUGE-L + + + Marco 0.376 0.389 0.318 0.38 + + + + + Train Domain + 0.40 + Literature 0.382 0.391 0.396 0.379 0.388 0.374 + 0.35 + 0.35 Healthcare 0.379 0.385 0.372 0.405 0.387 0.384 + 0.39 0.36 + + + 0.30 0.38 HotpotQA 0.383 0.369 0.345 0.34 + Legal 0.378 0.371 0.388 0.389 0.418 0.375 + 0.30 + 0.25 Others 0.383 0.376 0.381 0.372 0.378 0.392 + 0.37 + 0.32 + + + + + QA + + rco + + + + QA + ge + + + + + ot + Ma + 0.25 0.20 + + + + + tp + sa + + + + + Ho + s + Random Score-Desc Score-Asc CARROT Random Score-Desc Score-Asc CARROT + + + + + ic + + + ce + + + re + + + re + + al + + + s + + + + + Pa + er + em + + + + + g + tu + + + ca + er + + + + + Le + + + h + + + + + iki + a + m + + + + + lth + + + + + Ot + ad + + + + + er + Ordering Strategy Ordering Strategy + + + + + m + + + + + W + a + Ac + + + + + Lit + + + He + Co + Test Domain Test Dataset + + (a) Across LLMs (b) Across query types (a) Domain Generalization (b) Data Generalization + Fig. 10: Chunk order sensitivity analysis Fig. 11: Data and Domain Generalization Evaluation + benefits W (Φv ) during the simulation phase to assess chunk Query: Is bougainvillea a shrub? + + + combinations orders. Prior work [33], [38] has demonstrated 1)Bougainvillea is a tropical vining + shrub that comes in a wide array of Score + 3)During colder months, they are… + 2)Bougainvillea are tropical and Score + The flowers are actually modified + Score + leaves, called bracts, that are long + 0.43 0.21 0.27 + high correlations between reranker scores and end-task quality; bright and fanciful colors.... + 2)Bougainvillea are tropical and + Token + must be protected from frost... + 1)Bougainvillea is a tropical vining + Token lasting and bright... Bougainvillea Token + 899 899 are tropical and must be protected 8192 + we empirically verify this property in our setting by sam- must be protected from frost... + 3)During colder months, they are… + shrub that comes in a wide array of + bright and fanciful colors.... from frost... + + pling ∼50% queries from WikiPassageQA and enumerating CARROT Adversely Order NaïveRAG + + 2)Bougainvillea are tropical and Bougainvillea is a stunning tropical During colder months, they + diverse chunk combinations to collect reranker score, quality must be protected from frost... + Score vining shrub known for its vibrant Score + are… Bougainvillea are + Score + 0.33 0.32 0.23 + (ROUGE-L) pairs. As shown in Figure 8, we observe a N)The flowers are actually modified Token + array of bright and fanciful colors. + As a tropical plant, it requires + Token tropical and must be protected Token + leaves, called bracts, that are long 1011 8192 from frost... 8192 + Pearson correlation [68] of r=0.83, confirming that reranker- lasting and bright... + protection from frost… + + + assigned benefits reliably reflect downstream quality. CARROT w/o Agent RAPTOR HYDE + + + (2) Impact of Chunk Candidate Counts. As shown in Fig. 12: Case Study for CARROT + Figure 9, our analysis of chunk candidate counts on WikiPas- train the configuration agent on one domain’s training set and + sageQA demonstrates that CARROT achieves high response evaluate on other domains’ test sets using six distinct do- + quality while requiring fewer iterations and thus fewer external mains [25], creating a 6×6 generalization matrix (where each + model calls through its parallel expansion algorithm and pol- cell represents the ROUGE-L score as shown in Figure 11(a)). + icy tree search mechanism. Across different chunk candidate For dataset-level generalization, we train CARROT on one + counts, several methods require more external model calls to dataset and test on entirely different datasets (to verify cross- + select their optimal retrieval output, whereas CARROT shows dataset transferability, with ROUGE-L for WikiPassageQA + only a slight increase in external model call counts while and MARCO, F1 for HotpotQA presented in Figure 11(b)). + maintaining them within an acceptable range and achieving CARROT exhibits robust generalization across both dimen- + response quality superior to baselines. sions, maintaining competitive performance on training do- + (3) Sensitivity of Chunk Combination Order. To isolate mains while achieving comparable results on unseen domains + the effect of chunk combination order, we fix the chunk selec- and datasets, demonstrating practical applicability. + tion to top-5 chunks and vary only their ordering among four + strategies: Random, Score-Descending (highest-first), Score- D. Case Study + Ascending, and CARROT. As shown in Figure 10, CAR- Figure 12 presents qualitative examples comparing the re- + ROT outperforms the highest-first heuristic by 11–15% rel- trieval performance of CARROT against traditional baselines, + ative gain. Figure 10(a) shows consistent ∼11% performance illustrating how our method effectively addresses key chal- + gap between best and worst orderings across Llama3-8B, lenges: (1) Importance of Chunk Combination Order. To + DeepseekV3, and GPT-4o. Figure 10(b) reveals that multi-hop evaluate the impact of chunk combination order, we test a + queries exhibit higher order sensitivity (12% gap) than single- deliberately reordered version of CARROT’s output. The result + hop queries (11%), suggesting complex reasoning benefits shows a significant performance drop, with the ROUGE-1 + more from optimal ordering. score declining from 0.43 to 0.21, confirming that the chunk + 3) Generalizability Evaluation: To evaluate CARROT’s combination order is critical for RAG inference. + generalizability, we conduct experiments across both domain (2) Non-monotonic Utility of Chunk Selection. We compare + and dataset dimensions. For domain-level generalization, we CARROT with baselines such as RAPTOR, NaiveRAG, and + HYDE, all of which consume the full 8192 online input [2] J. Zhu, P. Cai, K. Xu, L. Li, Y. Sun, S. Zhou, H. Su, L. Tang, and Q. Liu, +tokens but underperform relative to CARROT, which uses “Autotqa: Towards autonomous tabular question answering through + multi-agent large language models,” Proc. VLDB Endow., vol. 17, no. 12, +only 899 online input tokens. For instance, NaiveRAG relies pp. 3920–3933, 2024. +on top-k retrieval with reranking but greedily selects chunks [3] P. Li, Y. He, D. Yashar, W. Cui, S. Ge, H. Zhang, D. R. Fainman, +without considering their correlations, often failing to capture D. Zhang, and S. Chaudhuri, “Table-gpt: Table fine-tuned GPT for + diverse table tasks,” Proc. ACM Manag. Data, vol. 2, no. 3, p. 176, +semantically relevant context. When answering the query ”Is 2024. +bougainvillea a shrub?”, it retrieves text containing match- [4] S. Arora, B. Yang, S. Eyuboglu, A. Narayan, A. Hojel, I. Trummer, +ing terms but misses the essential taxonomic information. and C. Ré, “Language models enable simple systems for generating + structured views of heterogeneous data lakes,” Proc. VLDB Endow., +Meanwhile, HYDE and RAPTOR depend on costly LLM vol. 17, no. 2, pp. 92–105, 2023. +calls to generate hypotheses or summarize content, leading [5] B. Snyder, M. Moisescu, and M. B. Zafar, “On early detection of +to inefficient token usage. In contrast, CARROT’s adaptive hallucinations in factual question answering,” in KDD, 2024, pp. 2721– + 2732. +chunk selection strategy retrieves concise, semantically aligned + [6] P. Sarthi, S. Abdullah, A. Tuli, S. Khanna, A. Goldie, and C. D. +context, enabling more accurate and efficient LLM responses. Manning, “RAPTOR: recursive abstractive processing for tree-organized +(3) Handling Query Diversity. To assess robustness to di- retrieval,” in ICLR, 2024. +verse query types, we compare CARROT with its variant [7] J. Fan, Z. Gu, S. Zhang, Y. Zhang, Z. Chen, L. Cao, G. Li, S. Madden, + X. Du, and N. Tang, “Combining small language models and large +CARROT w/o Agent, which lacks the adaptive configuration language models for zero-shot NL2SQL,” Proc. VLDB Endow., vol. 17, +agent. Without this component, the model struggles to identify no. 11, pp. 2750–2763, 2024. +optimal chunk combinations and often fails to retrieve the [8] Z. Wang, Z. Li, Z. Jiang, D. Tu, and W. Shi, “Crafting personalized + agents through retrieval-augmented generation on editable memory +most relevant information, demonstrating the importance of graphs,” in EMNLP, 2024, pp. 4891–4906. +the configuration agent in handling varied query intents. [9] E. Fons, R. Kaur, S. Palande, Z. Zeng, T. Balch, M. Veloso, and + S. Vyetrenko, “Evaluating large language models on time series feature + VIII. C ONCLUSION understanding: A comprehensive taxonomy and benchmark,” in EMNLP, + 2024, pp. 21 598–21 634. + Considering non-monotonic chunk utility, chunk correla- [10] Y. Ang, Y. Bao, Q. Huang, A. K. H. Tung, and Z. Huang, “Tsgassist: An +tion, and diverse query domains, we propose CARROT, a interactive assistant harnessing llms and RAG for time series generation + recommendations and benchmarking,” Proc. VLDB Endow., vol. 17, +learned cost-constrained retrieval optimization framework for no. 12, pp. 4309–4312, 2024. +RAG. We model chunk combination orders as a policy tree [11] S. Chen, J. Fan, B. Wu, N. Tang, C. Deng, P. Wang, Y. Li, J. Tan, F. Li, +and apply MCTS to find the optimal combination within J. Zhou, and X. Du, “Automatic database configuration debugging using + retrieval-augmented language models,” Proc. ACM Manag. Data, vol. 3, +a cost constraint. A configuration agent predicts the best no. 1, pp. 13:1–13:27, 2025. +query configuration and reranker, while a parallel expansion [12] S. Wang, X. Yu, M. Wang, W. Chen, Y. Zhu, and Z. Dou, “Richrag: +strategy processes multiple nodes per iteration. Comprehensive Crafting rich responses for multi-faceted queries in retrieval-augmented + generation,” CoRR, vol. abs/2406.12566, 2024. +experiments across three benchmark datasets demonstrate that [13] D. Edge, H. Trinh, N. Cheng, J. Bradley, A. Chao, A. Mody, S. Truitt, +CARROT significantly outperforms state-of-the-art methods and J. Larson, “From local to global: A graph RAG approach to query- +under constrained costs, achieving up to 30% improvement focused summarization,” CoRR, vol. abs/2404.16130, 2024. +while maintaining superior efficiency and scalability. [14] Y. Yu, W. Ping, Z. Liu, B. Wang, J. You, C. Zhang, M. Shoeybi, + and B. Catanzaro, “Rankrag: Unifying context ranking with retrieval- + augmented generation in llms,” in NeurIPS, 2024. + ACKNOWLEDGMENT [15] Z. Wang, S. X. Teo, J. Ouyang, Y. Xu, and W. Shi, “M-RAG: rein- + forcing large language model performance through retrieval-augmented + This research is supported by Singapore MOE AcRF Tier- generation with multiple partitions,” in ACL, 2024, pp. 1966–1978. +2 grant MOE-T2EP20223-0004. Wei Dong is supported by [16] D. Gao, H. Wang, Y. Li, X. Sun, Y. Qian, B. Ding, and J. Zhou, “Text- +the National Research Foundation, Singapore, and the Cyber to-sql empowered by large language models: A benchmark evaluation,” +Security Agency of Singapore under the National Cyberse- Proc. VLDB Endow., vol. 17, no. 5, pp. 1132–1145, 2024. + [17] Z. Jiang, F. F. Xu, L. Gao, Z. Sun, Q. Liu, J. Dwivedi-Yu, Y. Yang, +curity R&D Programme and the CyberSG R&D Programme J. Callan, and G. Neubig, “Active retrieval augmented generation,” in +Office (Award CRPO-GC3-NTU-001). Any opinions, findings, EMNLP, 2023. +conclusions, or recommendations expressed in these materials [18] A. Singh, A. Ehtesham, S. Kumar, and T. T. Khoei, “Agentic + retrieval-augmented generation: A survey on agentic RAG,” CoRR, vol. +are those of the author(s) and do not reflect the views of the abs/2501.09136, 2025. +National Research Foundation, Singapore, the Cyber Security [19] X. Yang, K. Sun, H. Xin, Y. Sun, N. Bhalla, X. Chen, S. Choudhary, +Agency of Singapore, or the CyberSG R&D Programme R. D. Gui, Z. W. Jiang, Z. Jiang, L. Kong, B. Moran, J. Wang, Y. Xu, + A. Yan, C. Yang, E. Yuan, H. Zha, N. Tang, L. Chen, N. Scheffer, +Office. We also thank Jingyi for helpful feedback. Y. Liu, N. Shah, R. Wanga, A. Kumar, S. Yih, and X. Dong, “CRAG + - comprehensive RAG benchmark,” in NeurIPS 2024, A. Globersons, + AI-G ENERATED C ONTENT ACKNOWLEDGMENT L. Mackey, D. Belgrave, A. Fan, U. Paquet, J. M. Tomczak, and + C. Zhang, Eds., 2024. + We use LLMs solely for language refinement, including [20] J. Wang, E. Hanson, G. Li, Y. Papakonstantinou, H. Simhadri, and +grammar checking, sentence rephrasing, and improving clarity. C. 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VLDB + Endow., vol. 17, no. 7, pp. 1765–1774, 2024. + \ No newline at end of file diff --git a/wizard-work/sphinx/archive-longctx-retrieval-2026-10-26/text/2412.18232v2.txt b/wizard-work/sphinx/archive-longctx-retrieval-2026-10-26/text/2412.18232v2.txt new file mode 100644 index 0000000..b38732d --- /dev/null +++ b/wizard-work/sphinx/archive-longctx-retrieval-2026-10-26/text/2412.18232v2.txt @@ -0,0 +1,1255 @@ + Efficient Long Context Language Model Retrieval with Compression + + + Minju Seo1 Jinheon Baek1 Seongyun Lee1 Sung Ju Hwang1,2 + KAIST1 , DeepAuto2 + {minjuseo, jinheon.baek, seongyun, sungju.hwang}@kaist.ac.kr + + + + + Abstract such as summarization or question answering over + long articles (Xu et al., 2024b; Kim et al., 2024). In + Long Context Language Models (LCLMs) have + emerged as a new paradigm to perform Infor- + addition to this, LCLMs go beyond these relatively +arXiv:2412.18232v2 [cs.IR] 28 May 2025 + + + + + mation Retrieval (IR), which enables the direct simple tasks to handle more complex tasks, such as + ingestion and retrieval of information by pro- Information Retrieval (IR) or Text-to-SQL, which + cessing an entire corpus in their single context, not only demand long-range context understanding + showcasing the potential to surpass traditional but also involve reasoning across multiple docu- + sparse and dense retrieval methods. However, ments or structured queries (Lee et al., 2024; An + processing a large number of passages within et al., 2024; Liu et al., 2024b; Xu et al., 2024b). + in-context for retrieval is computationally ex- + Furthermore, due to their impressive performance, + pensive, and handling their representations dur- + ing inference further exacerbates the process- they have established a new paradigm in LM uti- + ing time. To address this challenge, we aim to lization and task solving. For example, in IR tasks + make LCLM retrieval more efficient and poten- that we focus on, LCLMs are capable of processing + tially more effective with passage compression. an entire corpus with a large number of documents + Specifically, we propose a new compression along with a user query in their single context, lead- + approach tailored for LCLM retrieval, which is ing to more precise identification of relevant infor- + trained to maximize the retrieval performance + mation and further surpassing traditional sparse + while minimizing the length of the compressed + passages. To accomplish this, we generate the + or dense retrieval approaches in many cases (Lee + synthetic data, where compressed passages are et al., 2024). Yet, LCLMs face the limitation that + automatically created and labeled as chosen or the required computational resources scale with + rejected according to their retrieval success for the input length, which has been overlooked by + a given query, and we then train the proposed existing work. + Compression model for Long context Retrieval To tackle this challenge, we propose a more + (CoLoR) with this data via preference optimiza- efficient (and potentially more effective) method + tion while adding the length regularization loss + for LCLM retrieval. Specifically, instead of rely- + on top of it to enforce brevity. We perform ex- + tensive experiments on nine datasets, and show- ing on the original passages, our approach uses + case that CoLoR improves the retrieval perfor- compressed passages that retain the core informa- + mance by 6% while compressing the in-context tion while filtering our irrelevant details, leading + size by a factor of 1.91. Our code is available at: to a substantial reduction in input size. It is worth + https://github.com/going-doer/CoLoR. noting that, while there are existing compression + methods (Jiang et al., 2023b; Xu et al., 2024a) par- + 1 Introduction + ticularly designed for text generation or retrieval- + The context size of Language Models (LMs) refers augmented generation, they are largely suboptimal + to the maximum number of tokens that the model for retrieval since they are not trained to prioritize + can process in a single input, which has rapidly key elements crucial for precise retrieval, such as + expanded, growing from a few hundred to 128K, relevance to the query or fine-grained document + and recently reaching 1M tokens in Long Context distinctions. In contrast, our compression model + Language Models (LCLMs) (OpenAI, 2024; Team, is trained to optimize the LCLM retrieval perfor- + 2024; Anthropic, 2024). Notably, this expansion mance, while minimizing the passage length with + has unlocked new capabilities, enabling models to the regularization term added on top of the opti- + handle tasks that require extensive context lengths, mization objective. + + 1 + (A) Dense Retrieval (B) LCLM Retrieval (C) LCLM Retrieval w/ CoLoR (Ours) (D) Performance + Input + Raw Passage Top-1 Raw Passage Raw Passage + Raw Passage + ID 0: Major style ID 0 47.09 ID 0: Major style ID 0: Major style + ID 0: Major style guides + guides advise guides advise + advise consulting a dictionary + guides advise CoLoR (Ours) + consulting a dictionary consulting a dictionary consulting a dictionary + to determine whether a + to determine whether .. ID 1 46.02 to determine whether .. to determine whether .. + compound modifier .. + + + Doc Enc. + ID 1: The music was ID 1: The music was ID 1: The music was ID 1: The music was Input + composed by Howard ID 2 43.96 composed by Howard composed by Howard Shore, + LCLM composed by Howard LCLM + Compressed Passage + Shore, who previously Shore, who previously who previously composed the Shore, who previously + composed the first t… composed the first t… first two parts of the trilogy… composed the first t… ID 0: Style guides reco… + Dot Product + ID 2: On March 5, 2014, ID 2: On March 5, 2014, ID 2: On March 5, 2014, the ID 2: On March 5, 2014, ID 1: Howard Shore co .. + the College Board + for the College Board College Board announced its the College Board + announced its plan to Similarity score announced its plan to plan to redesign the SAT in announced its plan to + Output Output + ID 2: The College Board.. + Query Enc. + + + + + redesign the SAT in .. redesign the SAT in .. order to link the exam.. redesign the SAT in .. + ID 1 ID 2 + Query: When did the Query: When did the Query: When did the Query: When did the Query: When did the + sat become out of 1600? Wrong sat become out of 1600? Correct + sat become out of 1600? sat become out of 1600? sat become out of 1600? + + + +Figure 1: Comparison of different IR approaches. (A) Dense Retrieval. To identify relevant documents to the given query, it first +embeds them into the vector space and then calculates their semantic similarity. (B) LCLM Retrieval. The LCLM takes and +processes the raw passages from the corpus along with the query in the input context, and identifies the relevant passages. (C) +CoLoR. We compress the raw passages, and use the compressed passages alongside the query as the LCLM input for retrieval. + + Specifically, to train our compression model, we 5 single-document and 4 multi-document retrieval +leverage the strategy of Odds Ratio Preference Op- scenarios. On a battery of tests conducted, we then +timization (ORPO) (Hong et al., 2024), a method demonstrate that our approach not only improves +well-suited for maximizing the preference by com- the retrieval performance by 6% but also reduces +paring pairs of samples and learning to prefer one the context length for retrieval by a factor of 1.91 +over the other based on the specific objective. We over the baseline that uses the original passages, +note that this is particularly useful for our scenario and it is further superior to the existing text com- +since it allows us to rank compressed passages ac- pression (or summarization) methods. Further, we +cording to their retrieval performance, helping the show that our compression method can be general- +model distinguish between more and less effective izable to datasets not seen during its training. +compressions, without the need for generating the +ground-truth compression outputs manually. In 2 Related Work +other words, to generate the training data for it, we Information Retrieval The goal of Informa- +automatically create multiple compressed versions tion Retrieval (IR) is to fetch documents rele- +of each raw passage in the corpus and evaluate their vant to a query, which has evolved significantly +retrieval success with the LCLM retrieval outcome. with their application to various tasks, such as +Subsequently, compressed samples are labeled as web search and question answering. Early ap- +either chosen or rejected based on this evaluation, proaches used sparse retrieval methods, such as +which can ultimately guide the model toward gen- BM25 (Robertson and Zaragoza, 2009), which +erating the effective compression for passages for are based on lexical matching between queries +retrieval during preference optimization. However, and documents. As the field progressed, dense +using ORPO alone might not sufficiently reduce the retrieval techniques have developed, leveraging +output length of the compressed passages. Thus, text embedding models to capture richer seman- +to overcome this, we further introduce a dynamic tic relationships between queries and documents. +regularization term that adjusts the odds ratio loss Notable examples of dense retrievers include Sen- +in the training objective based on the length differ- tenceBERT (Reimers and Gurevych, 2019a), Dense +ence between chosen and rejected samples (where Passage Retrieval (DPR) (Karpukhin et al., 2020), +we additionally consider the compressed passages and SentenceT5 (Reimers and Gurevych, 2019b). +with the correct retrieval as rejected if there exist More recently, researchers have begun transform- +more shorter ones with the correct retrieval), which ing Large Language Models (LLMs) into retrieval +can encourage the model to prioritize brevity while systems (BehnamGhader et al., 2024), which aim +at the same time optimizing the retrieval accuracy. to utilize the vast contextual understanding capabil- +We refer to our approach as Compression for Long ity of LLMs in representing documents and queries. +Context Language Model Retrieval (CoLoR), il- Following this line of approaches, our work extends +lustrated in Figure 1 with previous IR methods. by utilizing LCLMs as the retrieval mechanism. + We experimentally validate the effectiveness of Long Context Language Models The recent ex- +CoLoR on 9 datasets for LCLM retrieval, including pansion in context length of LLMs, which is called + + 2 + Long Context Language Models (LCLMs), has em- vancements, previous work focusing on context +powered them to process and comprehend much compression in RAG or instruction-following tasks +larger amounts of information. Specifically, models is not well-optimized for retrieval tasks, as it does +like YaRN (Peng et al., 2024), Longformer (Beltagy not cater specifically to the needs of retrieval. To +et al., 2020), Gemini (Team, 2024), GPT-4 (Ope- address this gap, we propose an abstractive com- +nAI, 2024), and Claude (Anthropic, 2024) exem- pression model designed to improve the efficacy of +plify this advancement. In tandem with these devel- LCLM retrieval. +opments, new benchmarks have been introduced +to assess the capabilities of LCLMs across various Preference Optimization Aligning the language +tasks. For example, many studies (Bai et al., 2024; models with human preferences has become a key +Li et al., 2024a; Liu et al., 2024b; Yuan et al., 2024; focus in improving response generation (Ouyang +Wang et al., 2024) evaluated their performance et al., 2022; Zhao et al., 2023; Rafailov et al., +in long-context understanding, and there are also 2023; Hong et al., 2024). A prominent approach +other studies that focused on more specialized ar- is Reinforcement Learning from Human Feedback +eas such as code comprehension (Liu et al., 2024a) (RLHF) (Ouyang et al., 2022; Stiennon et al., 2020), +and training-free in-context learning (Bertsch et al., which leverages a reinforcement learning frame- +2025; Li et al., 2024b). Moreover, Lee et al. (2024) work where a policy model learns to evaluate and +demonstrated that LCLMs outperform traditional choose actions based on the state of an environment, +fine-tuned specialized models in several areas (such with human feedback acting as a reward signal. +as IR). However, despite these promising results, Notably, what sets these approaches apart is their +Liu et al. (2024b) highlighted the persistent chal- ability to train models using only preference selec- +lenges LCLMs face in fully grasping complex long tions on outputs, without needing explicit ground +contexts with high computational costs. In contrast truth answers. Additionally, Odds Ratio Preference +to existing work that has mainly explored the po- Optimization (ORPO) (Hong et al., 2024) further +tential and diverse applications of LCLMs, we take simplifies this by removing the requirement for a +a different direction on improving the efficiency of reference model during training, allowing single- +LCLMs in the context of IR by reducing the context step learning via preference selection of outputs. In +size while maintaining or enhancing performance. our approach, we apply this framework to train the + compression model without needing ground truth +Prompt Compression As LCLMs handle in- labels, enabling the model to learn based on a pair +creasingly longer contexts, the corresponding rise of compression outputs and their retrieval results. +in computational costs has sparked research into +methods for prompt compression. Extractive com- + 3 Methodology +pression is one common approach, where only the 3.1 Problem Statement +relevant tokens are retained. This often involves +techniques such as token pruning, which require as- We begin with formally explaining IR and LCLMs. +sessing the importance of individual tokens based Information Retrieval In a typical IR task, given +on specific metrics, for example, utilizing the self- a query q, its objective is to retrieve a ranked list +information or perplexity of the model (Jiang et al., k relevant entries from a corpus C, formulated as +2023b; Li et al., 2023). However, these methods follows: {di }ki=1 = Retriever(q, C), where di is +typically require access to the model’s internal pro- a document from C. The query q is typically tex- +cesses, making them feasible only for white-box tual, and C is a collection of documents. Tradition- +models. In contrast, abstractive compression meth- ally, Retriever is operationalized with the sparse +ods generate the condensed prompts without need- retrieval based on lexical term matching (Robert- +ing to preserve the original token order or structure, son and Zaragoza, 2009) or the neural embedding- +and can be applied to both black-box and white- based dense methods (Karpukhin et al., 2020). +box models as they do not rely on internal model +access. For example, Xu et al. (2024a) generate Long Context Language Model Retrieval Re- +compressed content from multiple documents in cently, Long Context Language Models (LCLMs) +Retrieval Augmented Generation (RAG) settings, have emerged with the ability to process extended +and Wang et al. (2023) use a similar approach to contexts, enabling them to handle inputs spanning +generate distilled documents. Despite these ad- dozens of documents. This capability has given rise + + 3 + 1. Compressed Passage Generation 2. Preference Data Labeling 3. Training + Raw Passage + Input + Compressed Passage 1 + ID 0: Most episodes feature a ID 0: The show features episo.. + storyline taking place in the ID 0: The show feat.. Output + present (2016 20132018, ID 1: The late 1870s saw the in.. + ID 0 + contemporaneous with airing) ID 1: The late 1870s.. ID 2: The Preamble of the U.S. .. + and a storyline taking place a.. LM 1 Output + ID 2: The Preamble .. Query: Why is kyoto removed + from target list? Preference CoLoR LCLM ID 2 + ID 1: The development of color + lithography in the late 1870s LCLM Data (Ours) + allowed the companies to create + attractive images to better Compressed Passage 2 Input Preferen + present their produc.. ID 0: The TV series .. ID 0: The TV series "This Is Us.. Data + Output + LM 2 ID 1: In the late 1870s, the … + ID 2: On May 30, Stimson asked ID 1: In the late 18.. ID 2 + Groves to remove Kyoto from ID 2: On May 30, Stimson .. + the target list due to its historical, ID 2: On May 30, .. Query: Why is kyoto removed + religious and cultural significance, + from target list? + but Groves .. Chosen Rejected + + +Figure 2: Overview of Training Processes for CoLoR. 1. We first create the training data for CoLoR by generating multiple +compressed passages from their original passages with multiple LMs. 2. The compressed passages and their associated query +are used as input to the LCLM, and their retrieval performance is measured to label them as either chosen or rejected based on +retrieval results. 3. CoLoR is trained using the pairs of chosen and rejected compressed passages obtained from previous steps. + + +to a new paradigm called LCLM Retrieval, which To address these limitations, we propose CoLoR +utilizes LCLMs to solve IR tasks (Lee et al., 2024). (Compression for Long Context Retrieval), which +To be formal, similar to the typical IR approaches, is a novel compression model designed for LCLM +LCLM retrieval aims to retrieve relevant documents retrieval. CoLoR generates compressed passages +from the corpus C for the query q, which can be by learning to balance two objectives: maintain- +represented as follows: {di }ki=1 = LCLM(T (q, C)), ing high retrieval accuracy and reducing passage +where T is the prompt template which serves as length. To achieve this, we leverage preference op- +the structured format that outlines the context for timization using synthetic preference data, where +LCLMs (including task descriptions) to direct them compressed passages are automatically generated, +in performing retrieval. It is worth noting that, un- and labeled based on their retrieval success as well +like traditional retrieval methods (sparse or dense), as their resulting lengths. This allows the model to +which involve pairing each document with a query distinguish between more and less effective com- +and calculating similarity scores to rank documents, pressions without manually collecting labels. +LCLM retrieval takes both the entire corpus and Formally, let di ∈ C represent a raw document +the query as the single input and directly identifies (or passage) from the corpus. Our goal is to apply +relevant documents within it. However, a signifi- the CoLoR model to compress each document di +cant challenge with LCLM retrieval lies in the use into a more concise representation ci = CoLoR(di ), +of raw documents (or passages) as input, as they of- where ci is the compressed version of the raw doc- +ten contain unnecessary, redundant, and irrelevant ument. Ideally, the compressed passage ci retains +context, leading to increased computational costs. the most relevant information while filtering out + unnecessary details, therefore, reducing the length +3.2 CoLoR: Compression for + of the input to the LCLM. After compressing every + Long Context Language Model Retrieval + document in the corpus, during the retrieval pro- +To tackle the inefficiency of using raw passages in cess, instead of directly using the original corpus +LCLM retrieval, we propose using compressed pas- C, the LCLM ingests the compressed corpus C ∗ = +sages to reduce the computational overhead with- |C| + {ci }i=1 , where each element in C is transformed by +out compromising retrieval effectiveness. There CoLoR. In other words, the retrieval process can be +are two common approaches to compressing pas- redefined as follows: {di }ki=1 = LCLM(T (q, C ∗ )) +sages: using prompt-based methods with LLMs or with |T (q, C ∗ )| ≪ |T (q, C)| where | · | measures +leveraging off-the-shelf compression models. How- the number of tokens in the resulting prompt. +ever, prompt-based methods often fail to achieve +optimal compression ratios and are not tailored to + 3.2.1 Training Recipe for CoLoR +enhance IR performance. Likewise, existing com- +pression models, which are typically designed for We now turn to explaining the details of how we +tasks other than IR (such as RAG or instruction train our CoLoR to optimize efficiency while im- +following), are not well-suited for LCLM retrieval. proving retrieval accuracy, illustrated in Figure 2. + + 4 + Data Collection To train CoLoR for LCLM re- ization term that adjusts the odds ratio loss based +trieval, we need to create a new dataset as no such on the length difference between rejected and cho- +datasets are available. Our data creation process sen samples. Specifically, we redefine the ORPO +begins with leveraging multiple LLMs to generate loss by multiplying it with a specific factor deter- +multiple compressed versions of raw documents (or mined as the length difference between yl and yw +passages), by prompting them with the prompt tem- (where yl is always longer than yw based on the +plate: T = Summarize the following content: criteria in our data collection process), as follows: +{passage}, formalized as follows: c = LLM(T (d)) +where c is the compressed passage and d is its orig- LCoLoR = E(q,yw ,yl ) [LSFT + λ · LOR · (|yl | − |yw |)] , +inal version. After that, the compressed passages + where | · | measures the length of the compressed +are used as inputs for the LCLM retrieval, which + passage. This extra regularization term directly +are then labeled as either chosen or rejected based + allows the model to make larger updates in cases +on two criteria: 1) whether the compressed passage + where the chosen sample is significantly shorter +is correctly retrieved in response to its associated + than the rejected one, making it to favor concise +query and 2) whether its length is shorter than any + outputs without sacrificing retrieval accuracy. +of the other successfully retrieved compressed pas- +sages. For instance, if several compressed versions 4 Experiment Setup +of a passage are retrieved correctly, the shortest +of these is labeled as chosen, while the others are 4.1 Datasets +labeled as rejected. Also, if the retrieval with the We evaluate the performance of CoLoR on 9 widely +compressed passage fails, it is labeled as rejected. used LCLM retrieval benchmark datasets, follow- +This allows us to create the dataset with pairs of ing the setup from Lee et al. (2024), including 5 +chosen and reject compression results for training single-document retrieval datasets: FEVER, FIQA, +CoLoR with preference optimization, which can MS MARCO, NQ, and SciFact (Thorne et al., +ultimately prioritize compressions that improve re- 2018; Maia et al., 2018; Nguyen et al., 2016; +trieval accuracy while minimizing passage length. Kwiatkowski et al., 2019; Wadden et al., 2020) and + 4 multi-document retrieval datasets: HotpotQA, +CoLoR Optimization To optimize our compres- + MuSiQue, QAMPARI, and QUEST (Yang et al., +sion model, we leverage the Odds Ratio Preference + 2018; Trivedi et al., 2022; Amouyal et al., 2023; +Optimization (ORPO) (Hong et al., 2024), an ap- + Malaviya et al., 2023). Note that single-document +proach designed for training models by comparing + retrieval tasks involve retrieving a single document +pairs of chosen and rejected samples without the + relevant to a query, whereas multi-document re- +need for a reference model. ORPO is particularly + trieval tasks require retrieving two or more docu- +suited for our task, as it allows us to directly opti- + ments. We provide the detailed statistics in Table 9. +mize the model to prefer compressed passages that +yield better retrieval performance with the shortest 4.2 Baselines and Our Model +length. Formally, the standard ORPO loss function + We evaluate CoLoR against baselines, as follows: +measures the odds ratio between the likelihood of + 1. Raw Passage is a standard approach for LCLM +generating the chosen response yw and the rejected + retrieval that directly uses raw passages; 2. Docu- +response yl , represented as follows: + ment Title uses only the titles of the passages with- + LORPO = E(q,yw ,yl ) [LSFT + λ · LOR ] , out the full content; 3. Zero-Shot Compression + uses LLMs to compress passages via prompting, +where LSFT is the supervised fine-tuning loss for for example, T = Summarize the following +the chosen response based on the causal language content: {passage}, with GPT-4o-mini (Ope- +modeling negative log-likelihood, and LOR is the nAI, 2024) and Phi3 (Abdin et al., 2024); 4. Selec- +loss for the odds ratio of the chosen response over tive Context is an extractive compression method +the rejected one (See Hong et al. (2024) for details). that selects tokens based on the self-information of + However, while ORPO enables effective prefer- the model (Li et al., 2023), where we use two com- +ence learning for making the compression model, pression rates: 0.3 and 0.6; 5. LLMLingua is an +it does not inherently reduce the length of the com- extractive compression method that selects tokens +pressed passages as much as desired. To overcome based on the perplexity scores (Jiang et al., 2023b); +this, we further propose to use a dynamic regular- 6. RECOMP is an abstractive compression method + + 5 + Table 1: Results on LCLM retrieval. Type refers to a compression type: ✗ denotes no compression, Ex. denotes extractive +compression, and Ab. denotes abstractive compression. † denotes multi-document retrieval. Comp. is the compression rate. + + FEVER FIQA MS MARCO NQ SciFact + Methods Type R@1 Comp. R@1 Comp. R@1 Comp. R@1 Comp. R@1 Comp. + + Raw Passage ✗ 0.95 1.00x 0.63 1.00x 0.90 1.00x 0.97 1.00x 0.57 1.00x + Document Title ✗ 0.97 31.65x N/A N/A N/A N/A 0.86 22.56x 0.68 13.98x + BM25 ✗ 0.93 1.00x 0.41 1.00x 0.78 1.00x 0.79 1.00x 0.75 1.00x + DPR ✗ 0.89 1.00x 0.31 1.00x 0.85 1.00x 0.94 1.00x 0.35 1.00x + Selective Context (0.6) Ex. 0.96 1.83x 0.35 1.83x 0.52 1.92x 0.95 1.88x 0.71 1.89x + Selective Context (0.3) Ex. 0.95 4.58x 0.15 4.24x 0.19 4.69x 0.90 4.91x 0.63 5.33x + LLMLingua Ex. 0.96 1.70x 0.46 1.6x 0.83 1.12x 0.97 1.24x 0.74 1.92x + Comp. w/ GPT Ab. 0.96 1.74x 0.68 2.09x 0.92 1.39x 0.99 1.37x 0.73 1.71x + Comp. w/ Phi Ab. 0.95 1.83x 0.68 2.31x 0.92 1.41x 0.99 1.39x 0.71 1.78x + RECOMP Ab. 0.96 3.02x 0.36 2.00x 0.75 1.77x 0.98 1.97x 0.70 2.31x + COMPACT Ab. 0.96 2.21x 0.48 2.56x 0.88 1.17x 0.98 1.32x 0.77 2.95x + CoLoR (Ours) Ab. 0.94 2.15x 0.73 2.82x 0.95 1.63x 0.98 1.62x 0.75 2.12x + + HotPotQA† MuSiQue† QAMPARI† QUEST† Average + Methods Type F1@2 Comp. F1@5 Comp. F1@5 Comp. F1@3 Comp. Perf. Comp. + + Raw Passage ✗ 0.87 1.00x 0.35 1.00x 0.56 1.00x 0.33 1.00x 0.68 1.00x + Document Title ✗ 0.65 12.52x 0.38 23.43x 0.25 22.48x 0.11 55.65x 0.60 26.04x + BM25 ✗ 0.82 1.00x 0.39 1.00x 0.76 1.00x 0.37 1.00x 0.64 1.00x + DPR ✗ 0.79 1.00x 0.46 1.00x 0.55 1.00x 0.31 1.00x 0.61 1.00x + Selective Context (0.6) Ex. 0.79 2.06x 0.37 1.93x 0.42 1.95x 0.19 2.01x 0.58 1.92x + Selective Context (0.3) Ex. 0.68 5.49x 0.37 5.10x 0.30 5.30x 0.11 5.41x 0.48 5.01x + LLMLingua Ex. 0.81 1.13x 0.36 1.23x 0.54 1.05x 0.21 1.75x 0.65 1.42x + Comp. w/ GPT Ab. 0.87 1.20x 0.40 1.32x 0.54 1.28x 0.32 1.95x 0.71 1.56x + Comp. w/ Phi Ab. 0.85 1.21x 0.41 1.39x 0.55 1.33x 0.31 2.03x 0.71 1.63x + RECOMP Ab. N/A 0.76x 0.38 1.00x 0.53 0.97x 0.21 1.50x 0.54 1.70x + COMPACT Ab. 0.87 1.14x 0.37 1.47x 0.52 1.51x 0.30 3.47x 0.68 1.98x + CoLoR (Ours) Ab. 0.86 1.37x 0.42 1.55x 0.55 1.50x 0.33 2.39x 0.72 1.91x + + +that compresses multiple documents designed for in the top k results) and Recall@k (the proportion +RAG scenarios (Xu et al., 2024a); 7. COMPACT is of relevant documents retrieved from up to k to- +an abstractive compression method that compresses tal). For compression efficiency, we compute the +and refines passages iteratively for question answer- compression rate (Comp.), defined as the average +ing (Yoon et al., 2024); 8. CoLoR (Ours) is our ab- number of tokens in raw passages divided by the +stractive compression method, trained to maximize average number of tokens in compressed passages. +the retrieval accuracy and minimize the compressed +passage length, with the preference optimization. 4.4 Implementation Details +Additionally, we also include BM25 (Robertson To ensure a fair comparison across all experiments, +and Zaragoza, 2009) (a sparse retriever that scores we use GPT-4o-mini as the underlying LCLM. We +documents based on term frequency and inverse use the Phi-3-mini-4k-instruct model as the base +document frequency) and DPR (Karpukhin et al., model for our compression method, CoLoR. For +2020) (a dense retriever that uses embeddings to the prompt, we structure it as a sequence of the +match queries and relevant passages) as the refer- corpus, 5-shot examples, and query, following Lee +ence to the performance of conventional retrievers, et al. (2024). Additional details are in Appendix A. +which are neither comparable nor our competitors. + 5 Experiment Results +4.3 Evaluation Metrics + Main Results We report main results in Table 1, +For single-document retrieval, we evaluate perfor- demonstrating that the proposed CoLoR approach +mance with Recall@1 (R@1), which measures consistently outperforms all baseline methods on +the proportion of queries for which the top-ranked both the single-document and multi-document re- +document is relevant. For multi-document, we use trieval tasks while at the same time substantially +F1@k, a metric combining Precision@k (the pro- compressing the input context size of LCLMs for +portion of correctly retrieved relevant documents retrieval. Specifically, CoLoR achieves a compres- + + 6 + Table 2: Results on out-of-domain datasets, where the target Table 3: Results on more challenging out-of-domain set- +retrieval category is excluded from the training of CoLoR*. tings, where models are trained on the datasets from the single + domain and then evaluated on the datasets from other domains. + Raw Passage Comp w/ Phi CoLoR* + Raw Passage Comp w/ Phi CoLoR* + Perf. Comp. Perf. Comp. Perf. Comp. + Training Evaluation Perf. Comp. Perf. Comp. Perf. Comp. + Fact-checking Fact- Multi-document + FEVER 0.95 1.00x 0.95 1.83x 0.95 2.14x checking HotPotQA 0.85 1.00x 0.85 1.21x 0.87 1.36x + SciFact 0.71 1.00x 0.71 1.78x 0.73 2.13x MuSiQue 0.35 1.00x 0.41 1.39x 0.40 1.54x + QAMPARI 0.56 1.00x 0.55 1.33x 0.56 1.48x + Average 0.83 1.00x 0.83 1.81x 0.84 2.14x QUEST 0.33 1.00x 0.31 2.03x 0.32 2.33x + Multi-document Argument + HotpotQA 0.85 1.00x 0.85 1.21x 0.87 1.36x ArguAna 0.28 1.00x 0.27 2.26x 0.33 2.72x + Touché-2020 0.76 1.00x 0.79 3.79x 0.82 4.56x + MuSiQue 0.35 1.00x 0.41 1.39x 0.40 1.54x + Average 0.52 1.00x 0.53 2.00x 0.55 2.33x + QAMPARI 0.56 1.00x 0.55 1.33x 0.56 1.48x + QUEST 0.33 1.00x 0.31 2.03x 0.32 2.33x Multi- Fact-checking + document FEVER 0.95 1.00x 0.95 1.83 0.95 2.14 + Average 0.52 1.00x 0.53 1.49x 0.54 1.68x SciFact 0.71 1.00x 0.71 1.78x 0.73 2.13x + Argument Argument + ArguAna 0.28 1.00x 0.27 2.26x 0.33 2.73x + ArguAna 0.28 1.00x 0.27 2.26x 0.34 2.73x Touché-2020 0.76 1.00x 0.79 3.79x 0.82 4.65x + Touché-2020 0.76 1.00x 0.79 3.79x 0.79 4.66x + Average 0.61 1.00x 0.61 2.34x 0.63 2.81x + Average 0.52 1.00x 0.53 3.03x 0.57 3.70x + 0.75 + GPT CoLoR (Ours) + 0.70 Raw +sion rate that reduces the input size by a factor of Passage COMPACT + + + + Retrieval Performance + 0.65 LLMLingua +1.91, while also improving retrieval performance +by 6%, compared to the standard approach with 0.60 + Selective Context (0.6) +raw passages. Also, our CoLoR provides the su- 0.55 RECOMP +perior quality compressed passages for retrieval, 0.50 + Selective Context (0.3) +compared to extractive and abstractive compression + 0.45 +models. For instance, when compared with extrac- 1 2 3 4 5 + Compression Rate +tive methods (Selective Context and LLMLingua), + Figure 3: The trade-off of different methods, showing their +CoLoR consistently demonstrates better retrieval compression rate (x-axis) and retrieval performance (y-axis). +performance. In addition to this, even against the +strong proprietary and open-source models (such + trieval category) and evaluating its performance on +as GPT and Phi3), CoLoR excels, particularly in + datasets from other domains (other retrieval cate- +terms of the compression rate, highlighting the limi- + gories). As shown in Table 3, the results demon- +tations of relying on prompting techniques to gener- + strate that our model generalizes (even in this chal- +ate compressed passages for retrieval. Lastly, when + lenging setting of) across different domains. +compared with multi-document compression meth- +ods such as RECOMP and COMPACT, our CoLoR Trade Off Between Compression Rate and Re- +significantly outperforms them in the retrieval per- trieval Performance To examine the trade-off +formance with the similar compression rate, which between the compression rate and retrieval perfor- +further confirms the necessity of task-specific train- mance, we visualize and analyze them in Figure 3. +ing for passage compression for LCLM retrieval. First of all, we observe that, while extremely high + compression rates (such as those achieved by using +Results on Out-of-Domain Datasets To assess the compression ratio of 0.3 with the Selective Con- +the generalizability of our compression approach text baseline) drastically reduce the input size, they +(CoLoR) on datasets not seen for training, we eval- also lead to significant information loss (potentially +uate its performance in out-of-domain settings by due to the removal of crucial information for re- +excluding a set of datasets from each retrieval cate- trieval), resulting in the retrieval performance drop +gory (such as fact-checking, multi-document, and of 20 on average compared to using the raw pas- +argument) from the training process and testing on sages. This observation highlights the critical trade- +them. As shown in Table 2, we observe that CoLoR off between compression and performance: simply +consistently enhances retrieval performance while maximizing compression for efficiency compro- +significantly reducing the input context size, which mises accuracy. In contrast, the proposed CoLoR +demonstrates the ability of our CoLoR to general- effectively balances this trade-off, ensuring that the +ize across diverse retrieval tasks and datasets. We reduction in context size does not sacrifice criti- +further conduct experiments by training CoLoR on cal information, thanks to our training strategy that +a single domain (i.e., datasets from the same re- guides the model to prefer the compressed passages + + 7 + Table 4: Analysis on computational costs of compression Table 5: Results with varying the base LMs for CoLoR, +strategies across different methods on the FIQA dataset. namely Phi3, Mistral, and Llama. Note that Average indicates + the average performance across all 9 datasets. Please refer to + # of Time Table 12 for results on other datasets. + Methods Base Models R@1 Comp. + Params. (Secs) + Comp. w/ Phi Phi3 mini 3.8B 1485.63 0.68 2.31x FIQA SciFact MuSiQue Average + COMPACT Mistral 7B 7.3B 3473.95 0.48 2.56x + R@1 Comp. R@1 Comp. F1@5 Comp. Perf. Comp. + CoLoR (Ours) Phi3 mini 3.8B 1290.82 0.73 2.82x + Phi3 mini 0.68 2.31x 0.71 1.78x 0.41 1.39x 0.68 1.63x + + CoLoR 0.67 2.82x 0.75 2.12x 0.42 1.55x 0.72 1.91x + 0.85 Compressed Passage (Ours) Mistral 7B 0.60 1.55x 0.73 1.46x 0.39 1.09x 0.69 1.21x + 0.80 Raw Passage + CoLoR 0.63 3.07x 0.80 3.03x 0.40 1.71x 0.71 2.25x + Document Title + Retrieval Performance + + + + + Llama 3.2 0.58 2.19x 0.70 2.17x 0.39 1.63x 0.69 1.84x + 0.75 + CoLoR 0.61 2.83x 0.72 3.03x 0.39 1.71x 0.69 2.15x + + 0.70 + Table 6: Results of an ablation study, where SFT refers to + 0.65 supervised fine-tuning, and ORPO w/ Reg refers to our full + 0.60 CoLoR model with the dynamic regularization term. Average + indicates the average performance across all 9 datasets. + 0% 20% 40% 60% 80% 100% + Normalized Position in Corpus MS MARCO MuSiQue QUEST Average + R@1 Comp. F1@5 Comp. F1@3 Comp. Perf. Comp. +Figure 4: Results with varying the position of (compressed) +passages associated with the query within the corpus, where Base Model 0.92 1.41x 0.41 1.39x 0.31 2.03x 0.71 1.63x + + SFT 0.88 1.42x 0.39 1.39x 0.32 2.18x 0.72 1.65x +0% (on the x-axis) represents beginning. + ORPO 0.94 1.59x 0.40 1.53x 0.31 2.31x 0.71 1.86x + + ORPO w/ Reg. 0.95 1.41x 0.42 1.55x 0.33 2.39x 0.72 1.91x + + +of successful retrieval over the ones with unsuccess- +ful retrieval (while enforcing brevity as well). ods. Yet, interestingly, our proposed compression + method (CoLoR) mitigates the lost-in-the-middle +Analysis on Compression Process In Table 4, issue, since it not only filters out irrelevant informa- +on the same hardware constraint, we find that our tion within passages during compression but also +CoLoR is faster in compressing the full corpus of allows for a more compact use of the input context. +the FIQA dataset (which takes around 20 minutes) +than other compression approaches, but also, with CoLoR with Different LMs To see whether the +the compressed passages from CoLoR, we achieve proposed CoLoR is versatile across different under- +the superior retrieval performance and compression lying LMs in generating compressed passages, we +ratio. Specifically, COMPACT has a longer com- vary them with three different LMs: Phi-3-mini-4k- +pression time, as it adopts an iterative approach to instruct, Mistral-7B-Instruct-v0.3, and Llama-3.2- +compress the text. In contrast, the compression pro- 3B-Instruct. Then, as shown in Table 5, we observe +cess with CoLoR requires minimal computational the consistent improvements of our CoLoR in both +burdens. We also note that the passage compres- compression rate and retrieval performance across +sion is a highly efficient process in terms of LCLM all models. This demonstrates that CoLoR and its +retrieval, as it can be performed only once, and its training methodology is not limited to a specific +outputs are reused (and cached) in inference. model but can be effectively generalized to others. + We provide results with all datasets in Table 12. +Analysis on Passage Position In Figure 4, we +analyze the position of passages within the input Ablation Study To see the effectiveness of each +context (associated with the query in the same con- component of our CoLoR, we perform an ablation +text), to see the potential lost-in-the-middle prob- study and present the results in Table 6. First of +lem (Liu et al., 2024b) in the context of LCLM re- all, we observe that, while Supervised Fine-Tuning +trieval: the retrieval performance can be decreased (SFT) yields strong retrieval performance, the com- +if the relevant passages to the query are placed pression rate remains comparable to the untrained +in the middle of the input sequence. To measure method. On the other hand, by utilizing preference +this, we place documents at intervals of 0%, 20%, optimization with Odds Ratio Preference Optimiza- +40%, 60%, 80%, and 100% across the input cor- tion (ORPO), we observe an improved compression +pus, and compare three different approaches: Raw rate, though this comes with a slight performance +Passage, Document Title, and our CoLoR. Then, degradation. However, the proposed dynamic reg- +similar to the finding in Liu et al. (2024b), placing ularization term (for compressed passage length) +documents towards the middle leads to a certain mitigates this trade-off, further improving both the +level of performance degradation across all meth- compression ratio and retrieval performance, reaf- + + 8 + Table 7: Results of different retrieval approaches with raw and compressed passages. In the first couple of columns, Types +refers to retrieval types, and Formats refers to corpus formats. Average indicates the performance over all 9 datasets. + + FEVER MS MARCO HotpotQA Average + Types Formats R@1 Comp. R@1 Comp. F1@2 Comp. Perf. Comp. + + BM25 Raw Passage 0.93 1.00x 0.78 1.00x 0.82 1.00x 0.67 1.00x + Comp. w/ GPT 0.91 1.74x 0.78 1.39x 0.80 1.20x 0.64 1.56x + CoLoR (Ours) 0.91 2.15x 0.70 1.63x 0.77 1.37x 0.62 1.91x + DPR Raw Passage 0.89 1.00x 0.85 1.00x 0.79 1.00x 0.61 1.00x + Comp. w/ GPT 0.89 1.74x 0.85 1.39x 0.77 1.20x 0.62 1.56x + CoLoR (Ours) 0.91 2.15x 0.88 1.63x 0.81 1.37x 0.63 1.91x + LCLM CoLoR (Ours) 0.94 2.15x 0.95 1.63x 0.86 1.37x 0.72 1.91x + + +Table 8: Manual evaluation results on three sampled pas- to ones in the raw passages, with randomly sam- +sages per dataset. We report the average number of total facts +(Facts), query-supportive facts (Sup. Facts), the proportion of pled three examples from each of all datasets with +supportive facts to total facts (Ratio), and the token count. two individual annotators. As shown in Table 8, + while the total number of facts in the compressed + Methods Facts Sup. Facts Ratio Tokens + passages decreases, the number of query-relevant + Raw Passages 14.13 1.91 13.52 210.93 + CoLoR (Ours) 9.26 1.74 18.80 93.41 + facts is only slightly reduced (from 1.91 to 1.74 + per passage on average). Also, when we look at + the proportion of relevant facts to total facts (Ra- +firming the overall efficacy of our proposed CoLoR tio), this proportion increases, indicating that the +approach in both the efficiency and effectiveness. compressed passages contain a higher density of +More detailed results are in Table 13 of Appendix. query-related atomic facts (while the proportion + of noisy, query-irrelevant information is reduced), +Adaptation of CoLoR to Conventional Retrieval which may support the performance improvement +In Table 7, we investigate how using compressed of our CoLoR. Additionally, we provide the case +passages impacts the performance of conventional study on the compressed passages in Figure 14. +sparse and dense retrievers, as they can bring an +additional benefit of faster indexing thanks to the re- 6 Conclusion +duced passage length. First, for the sparse retriever +(BM25), performance tends to decrease when us- In this work, we introduced Compression for Long +ing compressed passages, likely due to the loss of Context Langauge Model Retrieval (CoLoR), a +lexical information that BM25 relies on to match method specifically designed to improve efficiency +documents based on exact lexical similarities. In and effectiveness of LCLM retrieval by transition- +contrast, the dense retriever (DPR) shows perfor- ing from raw to compressed passages. Specifically, +mance improvements with compressed passages. the proposed CoLoR, trained with the synthesized +We conjecture that this might be because the un- preference data (based on retrieval outcomes of the +derlying LM for dense retrieval already contains compressed passages) and regularization loss for +much of the passage’s information within its pa- their lengths, optimizes both brevity and retrieval +rameters, and, as a result, compressing the passage performance. Through our extensive experiments +still retains essential details in making valuable rep- conducted across 9 datasets spanning single- and +resentations for it while additionally filtering out multi-document retrieval tasks, we demonstrated +irrelevant content (that might lead to noise in em- that CoLoR not only achieves a 6% improvement +bedding). However, despite these gains in dense re- in retrieval performance but also reduces context +trieval with the proposed CoLoR, the performance size by a factor of 1.91 over the standard LCLM +of LCLM retrieval coupled with CoLoR is substan- retrieval, which further surpasses existing text com- +tially better than conventional retrieval methods. pression methods. These highlight the significant + advantage of compressed passages to enhance ef- +Qualitative Analysis with Manual Evaluation ficiency for LCLM retrieval by reducing the com- +To see whether query-relevant information is pre- putational load and its associated costs, all while +served after passage compression, we manually even improving retrieval accuracy, making it more +compare atomic facts in the compressed passages scalable and practical for real-world applications. + + 9 + Limitations No. RS-2022-II220713, Meta-learning Applicable + to Real-world Problems). +While our proposed CoLoR approach demonstrates +significant advantages in LCLM retrieval, there are +still areas that future work may explore. First, fol- References +lowing the LCLM retrieval benchmark setup (Lee Marah Abdin, Jyoti Aneja, Hany Awadalla, Ahmed +et al., 2024), our experiments are conducted with Awadallah, Ammar Ahmad Awan, Nguyen Bach, +a maximum context length of 128K tokens, and, Amit Bahree, Arash Bakhtiari, Jianmin Bao, Harkirat +while this context length is indeed very large and Behl, Alon Benhaim, Misha Bilenko, Johan Bjorck, + Sébastien Bubeck, Martin Cai, Qin Cai, Vishrav +it has been increasingly extended further, in real- + Chaudhary, Dong Chen, Dongdong Chen, Weizhu +world applications, the size of the corpus can be Chen, Yen-Chun Chen, Yi-Ling Chen, Hao Cheng, +much larger (even after utilizing our compression Parul Chopra, Xiyang Dai, Matthew Dixon, Ro- +method), which may necessitate further modifica- nen Eldan, Victor Fragoso, Jianfeng Gao, Mei Gao, +tions of the overall LCLM retrieval framework. Yet, Min Gao, Amit Garg, Allie Del Giorno, Abhishek + Goswami, Suriya Gunasekar, Emman Haider, Jun- +developing the new process for LCLM retrieval is heng Hao, Russell J. Hewett, Wenxiang Hu, Jamie +beyond the scope of our work and we leave it as Huynh, Dan Iter, Sam Ade Jacobs, Mojan Javaheripi, +future work. Another consideration is the compres- Xin Jin, Nikos Karampatziakis, Piero Kauffmann, +sion process: it introduces an additional step before Mahoud Khademi, Dongwoo Kim, Young Jin Kim, + Lev Kurilenko, James R. Lee, Yin Tat Lee, Yuanzhi +retrieval; however, this is not a big deal as it only Li, Yunsheng Li, Chen Liang, Lars Liden, Xihui +needs to be performed once as like the indexing Lin, Zeqi Lin, Ce Liu, Liyuan Liu, Mengchen Liu, +process of sparse and dense retrieval approaches. Weishung Liu, Xiaodong Liu, Chong Luo, Piyush + Madan, Ali Mahmoudzadeh, David Majercak, Matt + Mazzola, Caio César Teodoro Mendes, Arindam Mi- +Ethics Statement tra, Hardik Modi, Anh Nguyen, Brandon Norick, + Barun Patra, Daniel Perez-Becker, Thomas Portet, +It is worth noting that, similar to any other retrieval Reid Pryzant, Heyang Qin, Marko Radmilac, Liliang +approaches, the retrieval corpus may contain harm- Ren, Gustavo de Rosa, Corby Rosset, Sambudha Roy, +ful or offensive content, and the compressed pas- Olatunji Ruwase, Olli Saarikivi, Amin Saied, Adil +sages could potentially reflect these biases. Also, Salim, Michael Santacroce, Shital Shah, Ning Shang, + Hiteshi Sharma, Yelong Shen, Swadheen Shukla, Xia +additional biases may be induced during the train- Song, Masahiro Tanaka, Andrea Tupini, Praneetha +ing process of LCLMs. Although addressing these Vaddamanu, Chunyu Wang, Guanhua Wang, Lijuan +concerns are obviously beyond the scope of our Wang, Shuohang Wang, Xin Wang, Yu Wang, Rachel +work, we acknowledge the importance of imple- Ward, Wen Wen, Philipp Witte, Haiping Wu, Xiaoxia + Wu, Michael Wyatt, Bin Xiao, Can Xu, Jiahang Xu, +menting the safeguards in future research to ensure + Weijian Xu, Jilong Xue, Sonali Yadav, Fan Yang, +that the retrieval process remains safe and fair. Jianwei Yang, Yifan Yang, Ziyi Yang, Donghan Yu, + Lu Yuan, Chenruidong Zhang, Cyril Zhang, Jianwen +Acknowledgements Zhang, Li Lyna Zhang, Yi Zhang, Yue Zhang, Yunan + Zhang, and Xiren Zhou. 2024. 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RE- + COMP: improving retrieval-augmented lms with con- + text compression and selective augmentation. In The + Twelfth International Conference on Learning Rep- + resentations, ICLR 2024, Vienna, Austria, May 7-11, + 2024. OpenReview.net. + +Peng Xu, Wei Ping, Xianchao Wu, Lawrence McAfee, + Chen Zhu, Zihan Liu, Sandeep Subramanian, Evelina + Bakhturina, Mohammad Shoeybi, and Bryan Catan- + zaro. 2024b. Retrieval meets long context large lan- + guage models. In The Twelfth International Con- + ference on Learning Representations, ICLR 2024, + Vienna, Austria, May 7-11, 2024. OpenReview.net. + +Zhilin Yang, Peng Qi, Saizheng Zhang, Yoshua Ben- + gio, William W. Cohen, Ruslan Salakhutdinov, and + Christopher D. Manning. 2018. Hotpotqa: A dataset + for diverse, explainable multi-hop question answer- + ing. In Proceedings of the 2018 Conference on Em- + pirical Methods in Natural Language Processing, + + + 13 + Table 9: Statistics of the data samples generated for preference Table 11: Results on the long context retrieval benchmark. +optimization for training CoLoR, which includes the number NQ 2WikimQA NarrativeQA Average +of samples per dataset, and the average length of reject and R@1 Comp. R@1 Comp. R@1 Comp. R@1 Comp. +chosen tokens. † denotes multi-document retrieval datasets. + Raw Passage 0.92 1.00x 0.42 1.00x 0.22 1.00x 0.52 1.00x + Dataset # of Samples Avg. Rejected Token Avg. Chosen Token Compressed Passage 0.95 63.02x 0.72 29.64x 0.56 555.93x 0.74 216.2x + + FEVER 483 236.19 152.63 + FIQA 455 148.15 88.22 + MS MARCO 1061 76.06 51.3 + NQ 635 158.16 111.67 + Torch (Paszke et al., 2019), Transformers (Wolf + SciFact 198 229.84 156.26 et al., 2020), SentenceTransformers (Reimers and + HotPotQA† 544 93.52 71.11 + MuSiQue† 12 107.58 80.5 Gurevych, 2019a), and BEIR (Thakur et al., 2021). + QAMPARI† 30 107.23 82.77 + Also, BM25 is implemented using a python library + Total 3418 135.61 91.36 + rank_bm252 , while, for DPR, we use a BEIR frame- +Table 10: Statistics of the benchmark retrieval datasets for work3 . Other baselines are sourced from publicly +experiments. † denotes multi-document retrieval datasets. available checkpoints on their repositories45 . + Avg. Avg. Comp. + Dataset # of Passage Comp. Ratio + Passage Token Passage Token + Datasets Details In Table 9, we provide the statis- + FEVER 588 169 78.60 2.15 + FIQA 531 190 67.43 2.82 tics of the data samples that we create for training + MS MARCO 1,174 76 46.76 1.63 + NQ 883 104 64.07 1.62 our compression model (CoLoR). Note that, among + SciFact 357 291 137.03 2.12 + HotPotQA† 1,222 69 50.43 1.37 all samples, we randomly use 3,077 samples for + MuSiQue† 824 112 72.22 1.55 + QAMPARI† 755 125 83.26 1.50 + training and 341 samples for validation. Also, Ta- + QUEST† 328 325 135.77 2.39 + ble 10 summarizes the retrieval datasets. We fol- + Average 740.22 162.33 81.73 1.91 + low the experimental setup from the LOFT bench- + mark (Lee et al., 2024), ensuring that the number +A Additional Experimental Setups of passages included in the LCLM context matches +Data Collection Details We collect compressed those in Lee et al. (2024). For all experiments, we +versions of passages to train CoLoR. The diversity use GPT-4o-mini as the underlying LCLM, which +of the compressed passages (including good and supports a context length of 128K tokens. +bad ones) is crucial to construct data for preference +optimization, and to ensure this, we prompt differ- B Additional Experimental Results +ent LLMs, such as Phi-3-mini-4k-instruct (Abdin Results on Long Context Retrieval Benchmark +et al., 2024), Mistral-7B-Instruct-v0.3 (Jiang et al., We further evaluate our CoLoR on the long con- +2023a), and Llama-3.2-3B-Instruct (Llama Team, text retrieval scenario, including two long context +2024), and GPT-4o-mini (OpenAI, 2024), with question-answering datasets from the LongEmbed +the same prompt: Summarize the following benchmark (Zhu et al., 2024) as well as the origi- +content: {passage}. nal corpus for the Natural Questions (NQ) dataset6 . +Fine-tuning Details For Supervised Fine-Tuning To enable comparisons between different methods, +(SFT), we use a learning rate of 5e-6. Similarly, the raw passages are truncated (as the context size +for ORPO with Phi and Llama, we use λ of 2.5 with original raw passages exceeds its limit) and +and a learning rate of 1e-6, while, for Mistral, we the compressed passages are generated using GPT- +change the learning rate to 5e-6. Also, all models 4o-mini (prompted to create summaries under 200 +are trained for 10 epochs with a batch size of 8, and words)7 . Then, as shown in Table 11, the compres- +the best epoch is selected based on the validation sion model reduces passage size by 216.2×, while +set. Lastly, we use the TRL library1 for training. increasing Recall@1 by 42%, compared to using + (truncated) raw passages, which further strengths +Computational Resources We perform training the effectiveness of our compression paradigm, par- +and inference on all baselines and our model by ticularly in handling lengthy passages. +using one of the NVIDIA RTX A6000 and NVIDIA 2 +RTX A5000 GPUs, depending on their availability. https://github.com/dorianbrown/rank_bm25 + 3 + https://github.com/beir-cellar/beir +With those GPUs, the time required to train our 4 + https://huggingface.co/cwyoon99/CompAct-7b +model over 10 epochs ranges from 3 to 5 hours. 5 + https://github.com/liyucheng09/Selective_Context + 6 + https://github.com/google-research-datasets/natural- +Deep Learning Libraries In our experiments, questions + 7 +we utilize the following deep learning libraries: Py- Due to the excessive length of passages for these datasets, + training CoLoR on them is not feasible within our computa- + 1 + https://github.com/huggingface/trl tional resources, and we leave this as a future work. + + + 14 + Table 12: Full results with all datasets by varying the base LM for CoLoR. † indicates multi-document retrieval datasets, and +* denotes out-of-domain datasets (that are not used for training CoLoR). + FEVER FIQA MS MARCO NQ SciFact + Methods R@1 Comp. R@1 Comp. R@1 Comp. R@1 Comp. R@1 Comp. + + Phi3 mini 0.95 1.83x 0.68 2.31x 0.92 1.41x 0.99 1.39x 0.71 1.78x + + CoLoR 0.94 2.15x 0.73 2.82x 0.95 1.63x 0.98 1.62x 0.75 2.12x + Mistral 7B 0.96 1.36x 0.58 1.55x 0.90 0.98x 0.98 1.01x 0.70 1.46x + + CoLoR 0.96 2.51x 0.63 3.07x 0.91 1.66x 0.98 1.78x 0.80 3.03x + Llama 3.2 0.96 2.03x 0.35 2.19x 0.52 1.47x 0.95 1.68x 0.71 2.17x + + CoLoR 0.95 2.30x 0.61 2.83x 0.86 2.76x 0.99 1.91x 0.72 2.64x + + HotPotQA† MuSiQue† QAMPARI† QUEST†* Average + Methods F1@2 Comp. F1@5 Comp. F1@5 Comp. F1@3 Comp. Perf. Comp. + + Phi3 mini 0.85 1.21x 0.41 1.39x 0.55 1.33x 0.31 2.03x 0.68 1.63x + + CoLoR 0.86 1.37x 0.42 1.55x 0.55 1.50x 0.33 2.39x 0.72 1.91x + Mistral 7B 0.83 0.92x 0.39 1.09x 0.55 1.07x 0.32 1.46x 0.69 1.21x + + CoLoR 0.85 1.43x 0.40 1.71x 0.56 1.68x 0.33 3.35x 0.71 2.25x + Llama 3.2 0.84 1.42x 0.39 1.63x 0.52 1.64x 0.31 2.33x 0.69 1.84x + + CoLoR 0.85 1.55x 0.40 1.84x 0.54 1.83x 0.30 2.68x 0.69 2.15x + +Table 13: Full results of the ablation study with all datasets. † denotes multi-document retrieval datasets, and * indicates +out-of-domain datasets (not used for training CoLoR). SFT refers to supervised fine-tuning, and ORPO w/ Reg denotes CoLoR. + FEVER FIQA MS MARCO NQ SciFact + Methods R@1 Comp. R@1 Comp. R@1 Comp. R@1 Comp. R@1 Comp. + + Base Model 0.95 1.83x 0.68 2.31x 0.92 1.41x 0.99 1.39x 0.71 1.78x + + SFT 0.95 1.82x 0.67 2.28x 0.88 1.42x 0.99 1.42x 0.80 1.83x + + ORPO 0.94 2.11x 0.64 2.75x 0.94 1.59x 0.99 1.59x 0.74 2.05x + + ORPO w/ Reg 0.94 2.15x 0.73 2.82x 0.95 1.63x 0.98 1.62x 0.75 2.12x + + HotPotQA† MuSiQue† QAMPARI† QUEST†* Average + Methods F1@2 Comp. F1@5 Comp. F1@5 Comp. F1@3 Comp. Perf. Comp. + + Base Model 0.85 1.21x 0.41 1.39x 0.55 1.33x 0.23 2.03x 0.71 1.63x + + SFT 0.88 1.22x 0.39 1.39x 0.57 1.33x 0.32 2.18x 0.72 1.65x + + ORPO 0.85 1.35x 0.40 1.53x 0.54 1.48x 0.31 2.31x 0.71 1.86x + + ORPO w/ Reg 0.86 1.37x 0.42 1.55x 0.55 1.50x 0.33 2.39x 0.72 1.91x + + +Full Results on Analyses In Table 12 and Ta- +ble 13, we provide the full results of varying the +base LM and the ablation study with all datasets, re- +spectively. Also, we provide the results of analysis +on passage position with all datasets in Figure 5. +Case Study We provide the case study on the +compressed passages generated by different ap- +proaches in Table 14, which shows that the com- +pressed passages from our CoLoR tend to leading +to the retrieval success and tend to be shorter. +Prompt Details For the prompt construction, we +follow the Corpus-in-Context prompting approach +from prior work (Lee et al., 2024). An example +prompt for the NQ dataset is provided in Table 15, +and, for more examples and details on the prompt, +please refer to Lee et al. (2024). + + 15 + Table 14: Case study on the retrieval sample from the FIQA dataset. +Methods + Passage Prediction +(# of Tokens) +Query If an index goes up because an underlying company issues more shares, what happens to the ETF + If a stock that makes up a big part of the Dow Jones Industrial Average decided to issue a huge + number of additional shares, that will make the index go up. At least this is what should happen, + since an index is basically a sum of the market cap of the contributing companies. No, indices + can have various weightings. The DJIA is a price-weighted index not market-cap weighted. An + alternative weighting besides market-cap and price is equal weighting. From Dow Jones: Dow Jones + Industrial Average2̆122. Introduced in May 1896, the index, also referred to as The Dow0̆0ae, is a + price-weighted measure of 30 U.S. blue-chip companies. Thus, I can wonder what in the new shares +Raw Passage + makes the index go up? If a stock is split, the Dow divisor is adjusted as one could easily see how 325818 ✗ +(315) + the current Dow value isn’t equal to the sum or the share prices of the members of the index. In + other cases, there may be a dilution of earnings but that doesn’t necessarily affect the stock price + directly as there may be options exercised or secondary offerings made. SO if the index, goes up, + will the ETF DIA also go up automatically although no additional buying has happened in the ETF + itself? If the index rises and the ETF doesn’t proportionally, then there is an arbitrage opportunity + for someone to buy the DIA shares that can be redeemed for the underlying stocks that are worth + more in this case. Look at the Creation and Redemption Unit process that exists for ETFs. + The Dow Jones Industrial Average (DJIA) is a price-weighted index of 30 U.S. blue-chip companies, + meaning its value is based on the share prices of its components rather than their market capitaliza- + tions. If a major stock in the DJIA issues a large number of new shares, the index should theoretically + rise, but adjustments like the Dow divisor may affect this relationship. When a stock splits, for +Comp. w/ GPT example, the divisor is adjusted to maintain the index’s integrity. If the index increases, it raises the + 325818 ✗ +(185) question of whether the corresponding ETF (DIA) will also rise automatically, despite no new buying + in the ETF. If the ETF’s price doesn’t align proportionally with the index’s increase, it may create an + arbitrage opportunity, allowing traders to profit by redeeming DIA shares for the underlying, more + valuable stocks. Understanding the creation and redemption unit process is essential for grasping + this dynamic. + The content discusses how the Dow Jones Industrial Average (DJIA) index, a price-weighted measure + of 30 U.S. blue-chip companies, can be affected by the issuance of additional shares by a major + company. It explains that the index value may not directly reflect the sum of the share prices due to +Comp. w/ Phi + adjustments in the Dow divisor. The content also addresses the relationship between the DJIA index 325818 ✗ +(138) + and the ETF DIA, stating that if the index rises but the ETF doesn’t proportionally, there may be an + arbitrage opportunity for someone to buy DIA shares and redeem them for the underlying stocks that + are worth more. The creation and redemption process of ETFs is briefly mentioned." + a stock that the IndustrialAverage to a of that will . least what, since is a market the., can have various + The index. Aning. From: Average™. in index also as Dowed ., can what the new shares is the Dow + is adjusted as one could see the value’t sum the the members of the index. In other cases, there may + be a dilution of earnings but that doesn’t necessarily affect the stock price directly as there may be +LLMLingua + options exercised or secondary offerings made. SO if the index, goes up, will the ETF DIA also go 325818 ✗ +(189) + up automatically although no additional buying has happened in the ETF itself? If the index rises + and the ETF doesn’t proportionally, then there is an arbitrage opportunity for someone to buy the + DIA shares that can be redeemed for the underlying stocks that are worth more in this case. Look at + the Creation and Redemption Unit process that exists for ETFs. +Selective of the Dow Jones Industrial Average to of, up. is what happen of of....: Dow Jones Industrial Average +Context (0.3) in, the to as is of., what up is, isn’t to of of., be ofn’t necessarily the stock price may be. up, up?n’t 482415 ✗ +(69) then is for to buy be for are more. at. + up a big part of the Dow Jones Industrial Average to a huge number of additional shares, that make + the index go up. is what happen, is a sum of the market cap of., can. is a price-weighted index + weighted. and is equal weighting.: Dow Jones Industrial Average in May, the also referred to as +Selective + is a price-weighted measure of 30 U.S. blue-chip companies., I can what go up? is, is as see the +Context (0.6) 418150 ✓ + current Dow valuen’t equal to the sum the share prices of the members of the index., there may be a +(175) + dilution of that doesn’t necessarily affect the stock price as there may be or made. up, will go up has + happened in the ETF itself? andn’t then there is an arbitrage opportunity for to buy be redeemed for + the underlying stocks that are more in this case. at for + The Dow Jones Industrial Average (DJIA) or "Dow 30" is a price-weighted average of 30 significant + stocks traded on the New York Stock Exchange (NYSE) and the Nasdaq. The DJIA is one of the +COMPACT most widely followed equity indices. The DJIA is calculated by summing the individual prices of + 325818 ✗ +(116) the 30 stocks in the index, then dividing that total by a divisor. If a stock that makes up a big part + of the Dow Jones Industrial Average decided to issue a huge number of additional shares, that will + make the index go up. + A stock issuing additional shares can cause the Dow Jones Industrial Average (DJIA) to rise due to + its price-weighted nature. However, the DJIA’s value isn’t directly equal to the sum of its member +CoLoR (Ours) + stocks’ prices. In cases of stock splits or dilution of earnings, the Dow divisor is adjusted. If the 418150 ✓ +(103) + DJIA rises and the ETF DIA doesn’t proportionally increase, there’s an arbitrage opportunity for + someone to buy DIA shares and redeem them for the underlying stocks worth more. + + + + 16 + Compressed Passage (Ours) + 1.0 + + 0.8 + + Retrieval Performance 0.6 + + 0.4 + + 0.2 + + 0.0 + 0% 20% 40% 60% 80% 100% + Normalized Position in Corpus + SciFact HotpotQA QUEST + NQ MuSiQue Average + FEVER QAMPARI + + Raw Passage + 1.0 + + 0.8 + Retrieval Performance + + + + + 0.6 + + 0.4 + + 0.2 + + 0.0 + 0% 20% 40% 60% 80% 100% + Normalized Position in Corpus + SciFact HotpotQA QUEST + NQ MuSiQue Average + FEVER QAMPARI + + Document Title + 1.0 + + 0.8 + Retrieval Performance + + + + + 0.6 + + 0.4 + + 0.2 + + 0.0 + 0% 20% 40% 60% 80% 100% + Normalized Position in Corpus + SciFact HotpotQA QUEST + NQ MuSiQue Average + FEVER QAMPARI + +Figure 5: Results with varying the position of (compressed) passages for all datasets. Specifically, we arbitrarily adjust the +positions of the gold and few-shot passages within the corpus relative to the query (0% represents the beginning). The figures at +the top, middle, and bottom represent the results with CoLoR, raw passage, and document title, respectively. + + + + 17 + Table 15: Example of corpus-in-context prompting for the NQ dataset, following Lee et al. (2024). The input is categorized by +type, with all types being provided as input to the LCLM for retrieval. + + Types + + You will be given a list of documents. You need to read carefully and understand all of them. Then you will be + given a query, and your goal is to find all documents from the list that can help answer the query. Print out the + ID and TITLE of each document. + + Your final answer should be a list of IDs, in the following format: + Instruction + Final Answer: [id1, id2, ...] + If there is only one ID, it should be in the format: + Final Answer: [id1] + + If there is no perfect answer output the closest one. Do not give an empty final answer. + ID: 0 | TITLE: English compound | CONTENT: Major style guides advise consulting a dictionary to determine + whether . . . | END ID: 0 + ID: 1 | TITLE: The Lord of the Rings: The Return of the King | CONTENT: The music was composed by + Corpus Howard Shore . . . | END ID: 1 + Formatting ... + ID: 881 | TITLE: Dexter (season 3) | CONTENT: While stalking a murderous drug dealer . . . | END ID: 881 + ID: 882 | TITLE: Interstellar medium | CONTENT: In the series of investigations . . . | END ID: 882 + ====== Example 1 ====== + Which document is most relevant to answer the query? Print out the TITLE and ID of the document. Then + format the IDs into a list. + If there is no perfect answer output the closest one. Do not give an empty final answer. + Few-shot query: where did the dewey decimal system come from + Examples The following documents can help answer the query: + TITLE: Dewey Decimal Classification | ID: 199 + Final Answer: [’199’] + ... + ====== Now let’s start! ====== + Which document is most relevant to answer the query? Print out the TITLE and ID of the document. Then + format the IDs into a list. + Query Formatting + If there is no perfect answer output the closest one. Do not give an empty final answer. + query: when does monday night raw come on hulu + The following documents can help answer the query: + + + + + 18 + \ No newline at end of file diff --git a/wizard-work/sphinx/archive-longctx-retrieval-2026-10-26/text/2501.08248v3.txt b/wizard-work/sphinx/archive-longctx-retrieval-2026-10-26/text/2501.08248v3.txt new file mode 100644 index 0000000..e55aedd --- /dev/null +++ b/wizard-work/sphinx/archive-longctx-retrieval-2026-10-26/text/2501.08248v3.txt @@ -0,0 +1,1296 @@ + Eliciting In-context Retrieval and Reasoning for + Long-context Large Language Models + + Yifu Qiu1 * , Varun Embar2 , Yizhe Zhang2 , + Navdeep Jaitly2 , Shay B. Cohen1 , Benjamin Han2 + 1 + University of Edinburgh, 2 Apple + {yifu.qiu, scohen}@ed.ac.uk , + {v_embar, yizhe_zhang, njaitly, ben.b.han}@apple.com + + + Abstract as agent trajectory modeling and planning (Zhao + et al., 2024; Zhang et al., 2024b), video captioning + Recent advancements in long-context lan- (Xue et al., 2024; Zhang et al., 2023), and text-to- +arXiv:2501.08248v3 [cs.CL] 9 Jun 2025 + + + + + guage models (LCLMs) promise to transform video generation (Wang et al., 2024b; Lin et al., + Retrieval-Augmented Generation (RAG) by + 2023). Recent advancements in long-context lan- + simplifying pipelines. With their expanded + context windows, LCLMs can process entire guage models (LCLMs) hold particular promise + knowledge bases and perform retrieval and rea- for reshaping the Retrieval-Augmented Generation + soning directly – a capability we define as (RAG) paradigm (Lee et al., 2024; Li et al., 2024). + In-Context Retrieval and Reasoning (ICR2 ). With their expanded context windows, LCLMs re- + However, existing benchmarks like LOFT of- duce reliance on complex pipelines required by + ten overestimate LCLM performance by pro- context-length limitations and simplify knowledge + viding overly simplified contexts. To address + updates by allowing context modifications. For + this, we introduce ICR2 , a benchmark that + evaluates LCLMs in more realistic scenarios instance, LCLMs can accommodate entire knowl- + by including confounding passages retrieved edge bases within the context windows, effectively + with strong retrievers. We then propose three serving as working memory for new queries. + methods to enhance LCLM performance: (1) Achieving this goal requires LCLMs to effec- + retrieve-then-generate fine-tuning, (2) retrieval- tively retrieve and reason within their “contextual + attention-probing, which uses attention heads knowledge base,” a capability we define as In- + to filter and de-noise long contexts during de- Context Retrieval and Reasoning (ICR2 ). How- + coding, and (3) joint retrieval head training + ever, existing benchmarks often fail to accurately + alongside the generation head. Our evalua- + tion of five well-known LCLMs on LOFT and evaluate this capability. For example, Needle-in-a- + ICR2 demonstrates significant gains with our Haystack (NIAH; Kamradt 2023) is a popular test + best approach applied to Mistral-7B: +17 and to determine whether a model can retrieve a “nee- + +15 points by Exact Match on LOFT, and +13 dle” (a specific fact or statement) randomly inserted + and +2 points on ICR2 , compared to vanilla into a “haystack” (a corpus). Yet, the semantic dis- + RAG and supervised fine-tuning, respectively. continuity between the needle and the haystack can + It even outperforms GPT-4-Turbo on most tasks + unintentionally reveal the needle’s location, mak- + despite being a much smaller model. 1 + ing the task overly simple. LOFT (Lee et al., 2024), + 1 Introduction the first large-scale benchmark for evaluating re- + trieval and reasoning within contextual knowledge + The ability of large language models to process bases, uses human-annotated relevant documents + long contexts has significantly expanded their ap- as the needle, and fills the haystack with randomly + plicability across various domains, including book- sampled documents from an external knowledge + level information retrieval (Ding et al., 2024; Jin base. However, this random sampling results in a + et al., 2024), summarization (Kim et al., 2024; context with virtually no confounding information + Saxena and Keller, 2024; Qiu et al., 2023), and that is relevant but misleading, causing LOFT to + question answering (Liu et al., 2024; Wang et al., significantly overestimate LCLM performance. + 2024a). They also enable more complex tasks, such To bridge this evaluation gap, we introduce ICR2 , + * + a novel and challenging benchmark designed to as- + Work done while the author was an intern at Apple. + 1 + Our code and datasets are available at https://github. sess LCLMs under more realistic conditions. ICR2 + com/apple/ml-icr2. builds upon KILT (Petroni et al., 2021), a compre- + hensive knowledge base sourced from Wikipedia. retrieval and reasoning capabilities of long- +Unlike LOFT, which relies on random sampling, context language models (LCLMs), demon- +ICR2 uses strong retrievers to select challenging strating that existing benchmarks overestimate +confounding documents, creating a more difficult model performance. +“haystack.” Experimental results reveal that current +LCLMs struggle on ICR2 , with exact match rates • We explore three novel methods to enhance +dropping by up to 51% compared to evaluations on LCLMs ranging from supervised fine-tuning, +LOFT. These findings underscore the significant inference-time approach, to model architec- +challenges LCLMs face in accurately performing ture modification. +in-context retrieval in realistic scenarios. • Our best approach, applied to a small LCLM, + We next explore improving LCLMs’ in-context tightens the performance gap with the Oracle +retrieval and reasoning capabilities. While RAG RAG while beating the other baselines. It even +demonstrates strong results, it remains hindered matches GPT-4 on both in-domain (ICR2 ) +by complex multi-stage pipelines. Encouraged by and out-of-domain benchmarks (LOFT), al- +recent studies such as LLM2Vec (BehnamGhader beit with a much smaller model size. +et al., 2024; Ma et al., 2024) showing that LCLMs +can be effectively adapted for accurate retrieval 2 Related Work +tasks, offering a more natural approach by enabling + Long-context large language models (LCLMs) +joint optimization of both retrieval and generation + have garnered significant attention for their ability +steps, we propose three approaches: (1) Retrieve- + to process extended sequences. Models like Long- +then-generate fine-tuning: Inspired by the distil- + former (Beltagy et al., 2020) and BigBird (Zaheer +lation of step-by-step reasoning abilities (Shridhar + et al., 2020) introduced sparse attention mecha- +et al., 2023; Hsieh et al., 2023), we train LCLMs + nisms to efficiently handle long context. Scaling ef- +to frame tasks as two-hop reasoning chains. In this + forts, exemplified by GPT-4 (Achiam et al., 2023), +formulation, LCLMs first retrieve relevant informa- + underscore the importance of expanding context +tion from the context and then generate the final + windows for the long-context tasks. Data engineer- +responses. (2) Retrieval-attention probing: At + ing methods (Fu et al., 2024; Xiong et al., 2024; Jin +inference time, we probe attention heads activated + et al., 2024), expanding positional encoding (Ding +for in-context retrieval (Wu et al., 2024) and use + et al., 2024), and the parameter-efficient fine-tuning +their top predictions to filter out confounders from + (Chen et al., 2024) have been effective. +lengthy contexts. (3) Joint retrieval head training: + LCLMs hold the promise in reshaping RAG +We introduce an architectural modification to equip + (Achiam et al. 2023; Jiang et al. 2023; Yang et al. +LCLMs with a dedicated retrieval head, enabling + 2024; Abdin et al. 2024). By replacing static +joint optimization of retrieval and generation. + knowledge bases with the contextual one, this new + We conduct extensive experiments using five paradigm simplifies the deployment by discarding +LCLMs on both LOFT and our ICR2 benchmark. intermediate components while enabling the direct +We compare our methods against baselines includ- updates on LCLM’s knowledge (Lee et al., 2024). +ing Vanilla RAG, Closed-book, Oracle RAG, and Unlike the existing works, we systematically +supervised fine-tuning (SFT). Notably, our best ap- evaluate the use of LCLMs for scenarios where +proach, applied to Mistral-7B with a 32K token knowledge bases are directly placed as the context +limit, has the best performance across the tasks. It with a novel benchmark, ICR2 . ICR2 is different +outperforms Vanilla RAG and SFT baselines by an with the popular long-context benchmark such as +average of +17 and +15 points measured by Ex- (Bai et al., 2024) by emphasizing in the contex- +act Match on LOFT, and by +13 and +2 points on tual confounders. We also show that with targeted +ICR2 , respectively. The approach achieves perfor- enhancements, small-scale LCLMs can achieve per- +mance comparable to the state-of-the-art GPT-4, formance comparable to state-of-the-art models. +despite using only 7B parameters. We also provide +in-depth analyses of our approaches. 3 Are LCLMs Competent for RAG? + Our key contributions are as follows: + 3.1 LLMs Are Sensitive to the Confounders + • We introduce ICR2 , a realistic and challeng- LOFT (Lee et al., 2024) introduces a Corpus-in- + ing benchmark for evaluating the in-context Context (CiC) approach for retrieval-augmented + NaturalQuestions HotpotQA FEVER WoW + 0.20 + 0.9 Phi-7B 0.9 Phi-7B 0.9 Phi-7B + Qwen-2-1.5B Qwen-2-1.5B Qwen-2-1.5B + Qwen-2-7B Qwen-2-7B 0.18 Qwen-2-7B + 0.8 Mistral-2-7B 0.8 Mistral-2-7B 0.8 Mistral-2-7B + LLaMA-3-8B LLaMA-3-8B LLaMA-3-8B + + + + Task Specific Metrics + 0.7 GPT-4 0.7 GPT-4 0.7 0.16 GPT-4 + + 0.6 0.6 0.6 + Phi-7B 0.14 + 0.5 0.5 0.5 Qwen-2-1.5B + Qwen-2-7B + Mistral-2-7B 0.12 + 0.4 0.4 0.4 LLaMA-3-8B + GPT-4 + 0.3 0.3 0.3 0.10 + 0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 + Confounding Ratio Confounding Ratio Confounding Ratio Confounding Ratio + + + +Figure 1: Task-specific performance of five LCLMs on 32K ICR2 test sets with varying confounding ratios. For all +tasks, a higher value of task-specific metric indicates a better performance. + +generation, integrating a large-scale external knowl- Corpus: { + PSG001: {p_1}, + Indexing + Relevant + PSG002: {p_2}, Context + KILT +edge base directly into the LLM’s context. How- PSG002: {p_3}, + … + Knowledge + Base + - + } +ever, LOFT operates under the assumption that Filtering + Merge & Shuf e + + + + - + +the context is free of confounders. In practice, a Contriever - + + - +corpus often contains confounders – documents Query: + When is + DPR + + DrQA + Retrieving + + Barry’s Confounding ICR2 + Context +related to the query but potentially leading to incor- birthday? BM25 + Retrieved + Context + Contextual + Corpus + BLINK +rect answers. For example, given the query “Who +is the 44th U.S. President?”, the corpus might in- Figure 2: Construction pipeline for ICR2 . Based on +clude documents about the other presidents, such as KILT, we use five strong retrievers to retrieve con- +“The 45th U.S. President is Donald Trump”, which founders to serve as the “haystack”, while the human- +could mislead the LLM. Consequently, LOFT’s de- annotated contexts are used as the “needle”. +sign reduces the complexities of using real-world +corpora for RAG, potentially overestimating the making them particularly difficult for LCLMs to +performance of LCLMs. handle. Consequently, benchmarks like LOFT that + overlook these “strong” confounders risk overes- + We show that LCLMs are indeed sensitive to timating + fl model performance, as confounders are +the confounders missed in LOFT. We construct prevalent in real-world scenarios. +multiple test sets with varying confounding ratios +– t0, 25%, 50%, 75%, 100%u – and evaluate the 3.2 ICR2 Benchmark +LCLMs under zero-shot settings. The confound- To address this limitation, we propose an alternative +ing ratio p denotes the proportion of confounding benchmark, ICR2 , which leverages strong retriev- +context that are selected by retrievers while filter- ers to identify and incorporate these confounders +ing out the gold provenance, with the remaining, into the contextual corpus used in CiC, providing +i.e., p100% ´ pq, randomly sampled from an ex- a more realistic and challenging evaluation frame- +ternal knowledge base. At p “ 0, all confounding work. We choose KILT (Petroni et al., 2021), a +passages in the contextual knowledge base are ran- comprehensive suite of benchmarks designed for +domly sampled, equivalent to the setup in LOFT knowledge-intensive NLP tasks, to be our exter- +(Lee et al., 2024). Conversely, at p “ 1, all con- nal knowledge base (Figure 2). KILT covers tasks +founders are selected by the retrievers. such as question answering (Kwiatkowski et al., + We evaluate five LCLMs with a context length of 2019; Yang et al., 2018), fact verification (Thorne +at least 32K tokens: Phi-3-7B (Abdin et al., 2024), et al., 2018), and dialogue completion (Dinan et al., +Qwen-2-1.5B/7B (Yang et al., 2024), Mistral-003- 2018), all paired with a single Wikipedia snap- +7B (Jiang et al., 2023), LLaMA-3-Instruct-8B shot. Each KILT instance xq, a, P ` y consists of +(Dubey et al., 2024) and GPT-4-Turbo (Achiam a query q, the reference answer a, and its prove- +et al., 2023). Our findings in Figure 1, demonstrates nances P ` “ tp1 , . . . , pm u, which is a set of rele- +that LCLM performance is highly sensitive to con- vant Wikipedia pages and specific locations within +founders, with performance generally degrading them that support the answer. +as the confounding ratio increases. This indicates Building on KILT, each ICR2 instance is rep- +that confounders in P ´ obtained via retrievers, as resented as xq, a, Cy, where q and a is a query- +in ICR2 , pose greater challenges for the models answer pair from KILT, and C is the contextual +compared to those randomly sampled in LOFT. knowledge base required to answer the query. +These retriever-selected passages are relevant to At test time, a standardized prompt template, +the queries but fail to lead to the correct answers, rag_promptrCs, is applied to the context, and the + Query: using the same process as above. For all retrievers, +Where are the giant redwoods located in California? we exclude retrieved passages overlapping with +Reference Answer: P ` or containing exact substring matches with a, +Humboldt County + and the remaining passages are collected to form +Contextual Knowledge Base: the confounding provenance set, P ´ . The contex- +ID: PSG001 +Title: 2010–11 California Golden Bears Men’s Basketball tual corpus C is then constructed by concatenat- +Team ing P ` with P ´ up to the maximum length, i.e., +Context: The 2010–11 California Golden Bears men’s bas- +ketball team represented the University of California, Berkeley + C “ rP ` ; P ´ s. Finally, we shuffle all items in C +in the 2010–11 NCAA Division I men’s basketball season. to remove position bias. +ID: PSG002 Table 7 compares statistics between LOFT and +Title: Redwood National and State Parks our benchmark ICR2 . An example of an ICR2 in- +Context: Scenes set on the forest moon Endor in Star Wars +were filmed in the Tall Trees Redwood Grove in the northern stance is provided in Table 1, and the rag_prompt +part of Humboldt County, though the majority of filming was template used is detailed in Table 8 in Appendix B. +in private and public forests near the town of Smith River ... +... [90 context are omitted for the simplicity] ... 3.3 State of LCLMs Performance on ICR2 +ID: PSG100 We evaluate five LCLMs that support 32K or longer +Title: Malibu Grand Prix +Context: ... and San Antonio, Texas. Palace operates addi- context length using LOFT (Lee et al., 2024) and +tional locations in Los Angeles, California and Dallas, Texas. ICR2 benchmarks, with each one tested with in- +The Redwood City, CA location closed on August 18, 2013 put up to 32K tokens. We include three baselines +and the San Antonio location closed on September 7, 2015. + for comparison: (1) Vanilla RAG, where the CiC +Table 1: An example ICR2 instance. We show the query prompt from Lee et al. (2024) is used; (2) Closed- +with the reference answer, and the constructed contex- book, where the entire context (C) is removed to +tual knowledge base. We highlight the true provenance evaluate LCLM performance based solely on para- +in blue and the confounding passages in red. metric knowledge; and (3) Oracle RAG, which + includes only the ground-truth relevant contexts + Z ˚ in C to estimate an upper-bound performance. +result is sent to an LCLM to generate the final Table 2 presents our results. Overall, we observe +answer for evaluation. To construct C, we first that the Vanilla RAG significantly outperforms the +include all provenances P ` from KILT as the posi- closed-book baseline for most models, except for +tive passages. For confounder P ´ , unlike LOFT’s Qwen-2-1.5B on the WoW dataset. This trend per- +random sampling approach, we run five strong re- sists even with the state-of-the-art GPT-4-Turbo, +trievers on queries formed by concatenating q with suggesting that external knowledge remains cru- +a to select Top-K results as the confounding pas- cial for knowledge-intensive tasks despite the large +sages to be included in C. parametric capacity of LCLMs. Furthermore, RAG + We show the pipeline for ICR2 creation in Fig- performance on ICR2 lags notably behind that on +ure 2. To eliminate bias in the retrievers’ pref- LOFT, reflecting ICR2 ’s greater complexity due to +erences during the construction process, we pool its more realistic context construction. Lastly, the +the Top-K predictions from all retrievers and uni- large performance gap between RAG and Oracle se- +formly sample passages from top to bottom until tups on both benchmarks underscores the impact of +reaching the maximum allowable size for the con- confounding information, which hampers accurate +textual knowledge base. The retrievers used in- retrieval and response generation in LCLMs. +clude Contriever (Izacard et al.), DPR (Karpukhin +et al., 2020), DrQA (Chen et al., 2017), BM25 4 Eliciting ICR2 for LLMs +(Robertson and Jones, 1976; Robertson et al., +2009), and BLINK (Wu et al., 2020). For passage- 4.1 Retrieve-then-generate Fine-tuning +level retrievers such as Contriever, BM25, and Compared to the standard supervised fine-tuning +DPR, as input we chunk all documents in KILT’s where a model is asked to generate a Direct Answer +knowledge base and exclude passages overlapping (DA) to a given query, our first proposal, retrieve- +with any annotated provenance in P ` . We also then-generate, is a two-step process: the model +set K “ 200 for the 32K version of ICR2 . For first retrieves relevant passages from context and +document-level retrievers like DrQA and BLINK, then generates the final answer based on the context +we set K “ 20, and chunk retrieved documents and retrieved passages, all in one decoding pass. + LOFT ICR2 + All + NQ HotpotQA MUSIQUE Avg NQ HotpotQA FEVER WoW Avg + Closed-book + Qwen-2-1.5B-32K 0.21 0.22 0.03 0.15 0.29 0.24 0.62 0.15 0.32 0.25 + Qwen-2-7B-32K 0.31 0.30 0.07 0.23 0.35 0.30 0.71 0.15 0.38 0.31 + Mistral-2-7B-32K 0.46 0.38 0.05 0.30 0.49 0.38 0.73 0.14 0.43 0.38 + Phi-3-7B-128K 0.39 0.35 0.09 0.28 0.46 0.32 0.73 0.13 0.41 0.35 + LLaMA-3-instruct-8B 0.41 0.19 0.01 0.20 0.44 0.19 0.53 0.14 0.32 0.27 + GPT-4-turbo 0.58 0.57 0.28 0.48 0.61 0.50 0.85 0.16 0.53 0.51 + Vanilla RAG + Qwen-2-1.5B-32K 0.61 0.43 0.12 0.39 0.35 0.32 0.78 0.12 0.39 0.39 + Qwen-2-7B-32K 0.79 0.61 0.29 0.56 0.57 0.47 0.88 0.13 0.51 0.53 + Mistral-2-7B-32K 0.64 0.62 0.27 0.51 0.53 0.50 0.81 0.13 0.49 0.50 + Phi-3-7B-128K 0.76 0.68 0.41 0.62 0.60 0.50 0.90 0.14 0.53 0.57 + LLaMA-3-instruct-8B 0.45 0.56 0.16 0.39 0.53 0.45 0.83 0.15 0.49 0.45 + GPT-4-turbo 0.85 0.78 0.51 0.71 0.67 0.64 0.92 0.15 0.59 0.65 + Oracle RAG + Qwen-2-1.5B-32K 0.80 0.76 0.44 0.67 0.77 0.65 0.86 0.15 0.61 0.63 + Qwen-2-7B-32K 0.88 0.81 0.56 0.75 0.83 0.80 0.91 0.17 0.68 0.71 + Mistral-2-7B-32K 0.89 0.81 0.41 0.70 0.83 0.81 0.94 0.18 0.69 0.70 + Phi-3-7B-128K 0.90 0.85 0.63 0.79 0.87 0.81 0.91 0.14 0.68 0.73 + LLaMA-3-instruct-8B 0.81 0.77 0.60 0.73 0.80 0.74 0.86 0.19 0.65 0.68 + GPT-4-turbo 0.88 0.87 0.72 0.82 0.79 0.82 0.94 0.18 0.68 0.74 + +Table 2: Performance evaluation of six LCLMs on LOFT and ICR2 benchmarks. All models benefit from the Vanilla +RAG approach, but a gap remains between vanilla and oracle RAG performance. + +Formally, we train an LCLM to optimize, 4.2 Retrieval Attention Probing + ÿ Our second proposal, Retrieval Attention Probing + ppy | q, cq “ ppy | q, c, zi qppzi | q, cq, (1) (RAP), is an inference-time approach compatible + zi PZ with LCLMs without requiring re-training. Build- + ing on Wu et al.’s findings that specific attention +where q, y is the query and target, respectively, c heads are highly active during retrieval tasks (e.g., +is the contextual knowledge base, and Z is the NIAH), RAP utilizes these retrieval-focused atten- +collection of all relevant passages necessary for an- tion heads for context filtering before generating +swering q. This objective can be easily integrated responses. For each attention head h and query +into the next-token prediction task trained with the q, we track the Top-M attention scores AM h pqq. +maximum likelihood estimation, just by sequen- ChM pqq, representing the M passages correspond- +tially executing the retrieval and generation in a ing to AMh pqq, are then selected, and the hit rate +single forward pass. The overall loss is given as for head h is calculated as follows: + N + N + 1 ÿ |ChM pqq X Z ˚ pqq| + 1 ÿ HitRateh “ , (3) +L“ plog ppZi˚ |qi , ci q ` log ppyi˚ |Zi˚ , qi , ci qq , N i“1 |Z ˚ pqq| + N i“1 + (2) where N is the number of validation samples, and +where N is the number of training samples, and Z ˚ pqq is the set of all relevant contexts given query +Zi˚ “ tzi,1 + ˚ , z˚ , . . . , z˚ + i,2 i,|Zi˚ | + u is the collection of all q. Finally we select Q heads with the top hit rates +relevant passages for the query qi and contextual to be the retrieval heads as follows, +knowledge base ci . ÿ + Hret “ arg max HitRateh , (4) + We use two variants of retrieve-then-generate HĎt1,...,Hu,|Hret |“Q hPH +fine-tuning, both using the special tokens + and to delineate the where H is the number of attention heads, and Hret +retrieval step. In the first, Retrieve-Then-Answer is the set of Q attention heads with the top hit rates +(RTA), the model copies relevant passages. In the selected as the retrieval heads for context filtering. +second, Cite-Context-ID (CCI), the model gener- Note that our definition of retrieval heads dif- +ates only the IDs of the relevant passages. Figure 8 fers from (Wu et al., 2024) in two key ways: (1) +shows the templates for all variations. Instead of focusing solely on the Top-1 passages, + we allow each head to retain the Top-M , which S “ t. . . si . . . u, we can finally identify the indices +is particularly important for multi-hop reasoning T of the selected passages as the ones that receive +tasks requiring multiple passages for reasoning. (2) the top K scores, +We evaluate attention heads using the retrieval hit +rate, a more direct metric for our downstream tasks. T “ TopKpS, Kq. + During inference, we union all passages selected +by all retrieval heads, Hret , We then set the binary mask M for filtering the + ď original context C down to C ˚ , which is then used + C˚ “ Top-M αh pcq, (5) to prompt the LCLM to produce the final response. + hPHret cPC + 5 Experiment +where αh pcq is the attention score of head h on + Benchmarks. We use LOFT (Lee et al., 2024) +passage c, and C ˚ is the set of the Top-M selected + and our ICR2 benchmarks to evaluate LCLMs’ in- +passages from all heads Hret . We use C ˚ to form + context retrieval and reasoning capabilities. LOFT +a new CiC-style prompt and proceed to generat- + tests retrieval, single- and multi-hop question an- +ing the final response. Since |C ˚ | ! |C|, the final + swering, and reasoning using NaturalQuestions +decoding is actually performed on a filtered contex- + (Kwiatkowski et al., 2019), HotpotQA (Yang et al., +tual knowledge base with a much smaller length. + 2018), and MuSiQue (Trivedi et al., 2022). ICR2 +4.3 Joint Retrieval Head Training uses NaturalQuestions and HotpotQA, and addi- +Our final proposal introduces a dedicated retrieval tionally includes FEVER (Thorne et al., 2018) for +head to the LCLM model architecture. During in- fact verification and WoW (Dinan et al., 2018) for +ference, the model first uses the head to identify dialogue completion. Similar to LOFT, we report +relevant passages, after which the generation head average scores across 100 test cases per task us- +decodes a response conditioned on the retrieved ing the 32K context length versions, which is the +content. During training, the retrieval and genera- maximum supported by all tested LCLMs. +tion heads are jointly optimized using the Gumbel- Metrics. For the question answering and fact veri- +TopK trick (Kool et al., 2019), which mitigates the fication tasks, we use the exact match in (Lee et al., +non-differentiability of the retrieval process. 2024; Adlakha et al., 2024). We use ROUGE (Lin, + Figure 9 illustrates the modified model architec- 2004) to assess on dialogue completion. +ture. The retrieval head generates a binary mask, Training Details. We use ICR2 ’s training set to +M P t0, 1u|C| , indicating which passages to select fine-tune all models. Specifically, we randomly +(1) or ignore (0). The selected passages are then sample 7500, 7500, 5000, and 5000 instances for +passed to the generation head for response gener- NaturalQuestions, HotpotQA, FEVER, and WoW, +ation. The retrieval head consists of: (1) encoders respectively. To verify the effectiveness of our +for the query and passage, using the LCLM’s final proposed methods, we focus our experiments on +hidden states, and (2) a scoring layer that computes Mistral-Instruct-7B model (Jiang et al., 2023). +relevance scores by concatenating their encoded Baselines. We compare our approaches with +vectors. The top K passages are then selected for the baselines in Sec. 3.3: Vanilla RAG, Closed- +the generation head to produce a response. book, and Oracle RAG. We include the traditional + More specifically, let q denote a query and C “ ranking pipeline where we use TinyBERT (Jiao +tc1 , c2 , . . . , cn u be the set of n passages. For each et al., 2020) and MiniLM (Wang et al., 2020) fine- +pair pq, ci q, two single-layer encoders pool the final tuned on MS Marco passage retrieval corpus (Bajaj +hidden states from an LCLM and generate a query et al., 2016) to select the top-relevant passages for +vector, hQ , and a passage vector, hci : LCLMs. We report their retrieval performance in + Appendix D. We include the direct-answer SFT + hq “ encq pqq; hci “ encc pci q, (SFT-DA) which follows Zhang et al. by con- + catenating the confounders with gold documents +Each pair phq , hci q is then concatenated to form for SFT, and our methods in Sec. 4 include two +an input vector vi “ rhq ; hci s. A single scoring retrieve-then-generate SFT variants — Retrieve- +layer then predicts a scalar relevance score si for then-Answer (SFT-RTA), and Cite-Context-ID +each pair, essentially computing si “ f pvi q where (SFT-CCI) — and the joint retrieval head training +f p¨q is our scoring function. With the set of scores (RetHead) and retrieval attention probing (RAP). + Models & Methods LOFT ICR2 + NQ HotpotQA MUSIQUE NQ HotpotQA FEVER WoW + GPT-4 w/ + Close-book 0.58 0.57 0.28 0.61 0.50 0.85 0.16 + Vanilla RAG 0.85 0.78 0.51 0.67 0.64 0.92 0.15 + Oracle 0.88 0.87 0.72 0.79 0.82 0.94 0.18 + Mistral-7B w/ + Close-book 0.46 0.38 0.05 0.49 0.38 0.73 0.14 + Vanilla RAG 0.64 0.62 0.27 0.53 0.50 0.81 0.13 + RAG w/ RTA Prompting 0.60 0.70 0.27 0.54 0.51 0.83 0.15 + Re-ranking Strategy + w/ TinyBERT pk “ 8q 0.88 0.78 0.29 0.52 0.47 0.88 0.12 + w/ MiniLM pk “ 8q 0.83 0.77 0.27 0.51 0.46 0.86 0.13 + w/ TinyBERT pk “ 32q 0.87 0.87 0.33 0.62 0.51 0.91 0.13 + w/ MiniLM pk “ 32q 0.84 0.84 0.39 0.61 0.47 0.92 0.12 + Oracle RAG 0.89 0.81 0.41 0.83 0.81 0.94 0.18 + Supervised Fine-tuning + SFT-Direct Answer 0.70 0.65 0.25 0.59 0.70 0.90 0.22 + SFT-Retrieve-then-Answer 0.74 0.69 0.33 0.60 0.67 0.91 0.22 + SFT-Cite-Context-ID 0.76 0.54 0.35 0.63 0.63 0.89 0.21 + Joint Retrieval Head Training + RetHead w{Lgen ` Lret 0.15 0.13 0.07 0.48 0.54 0.9 0.21 + RetHead w{Lgen - - - 0.28 0.25 0.82 0.18 + RetHead w{Lret - - - 0.39 0.44 0.82 0.13 + Retrieval-Attention Probing + SFT-DA w/ RAP 0.78 0.76 0.47 0.64 0.67 0.89 0.21 + SFT-RTA w/ RAP 0.85 0.79 0.39 0.63 0.71 0.92 0.23 + +Table 3: Main results on LOFT (Lee et al., 2024) and ICR2 for our methods applied on Mistral-2-7B-Instruct (Jiang +et al., 2023). We also report the GPT-4 performance in the top panel. We highlight the improved and worsen +performances compared with Vanilla RAG, and bold the best method based on Mistral-7B, except the Oracle. +5.1 Main Results 4%, 1%, and 1% on NaturalQuestions, HotpotQA, + FEVER, and WoW, respectively. It also achieves +As shown in Table 3, all SFT variants outperform + top performance on 5 out of the 7 tasks, demonstrat- +the Vanilla RAG on both benchmarks, indicating + ing its superiority.Remarkably, it achieves compa- +that LCLMs struggle to effectively leverage con- + rable performance with the state-of-the-art GPT- +text as a knowledge base for RAG tasks. Further- + 4-Turbo on LOFT and ICR2 while using a much +more, the gap between SFT-DA and the Oracle + smaller model. Finally, RAP decoding is more +RAG highlights that supervised fine-tuning alone + effective for SFT models than with the original +is insufficient to achieve optimal results. + model, as SFT better activates retrieval-specific at- + We first apply RTA-style generation as a prompt- tention heads for the approach (see Sec. 6.3). To +only method (RTA Prompting). The resulting per- verify the generalization of methods across various +formance gains, though modest, demonstrate the LLMs, we conduct experiments with LLaMA-3- +potential of decoupling the generation process into Instruct (Dubey et al., 2024) in Appendix C: we +separate retrieval and generation stages. Among observe the similar improvements for retrieve-then- +the SFT variants, SFT-RTA in general outperforms generate training, and RAP decoding. +the others with an average improvement of 2%. +Specifically on LOFT benchmark, both SFT-RTA Compared to the traditional pipeline, LCLMs +and SFT-CCI outperform SFT-DA with 6% and 2% can effectively unify in-context retrieval and gen- +improvement on average, respectively, indicating eration, particularly on challenging benchmarks +the retrieve-then-generate strategy helps. On ICR2 , such as ICR2 . This highlights the advantage of an +however, all SFT variants perform the same. This end-to-end approach, wherein LCLMs contextually +demonstrates that ICR2 is a more discriminative select retrieved items based on both the query and +benchmark than LOFT. the evolving generation. + We apply RAP to all SFT models. On aver- For the joint retrieval head training (RetHead), +age, RAP enhances the models significantly, with we performed experiments on training only the +SFT-DA + RAP improving by 6% and SFT-RTA + generation head (w{Lgen ), only the retreieval head +RAP by 8%. The best-performing approach, SFT- (w{Lret ), and both (w{Lgen ` Lret ). The last vari- +RTA + RAP, achieves notable gains on the challeng- ant achieves the best performance, outperforming +ing ICR2 benchmark, with improvements of 3%, the Vanilla RAG baseline on ICR2 . However, it is + NaturalQuestions HotpotQA FEVER WoW Avg. + 0.64 0.70 0.60 + + + + Task Specific Metrics + 0.8 0.20 + 0.62 0.68 0.55 + 0.60 0.15 0.50 + 0.66 0.6 + 0.58 + 0.10 0.45 + 0.56 0.64 0.4 + 0.40 + 10000 15000 20000 25000 10000 15000 20000 25000 10000 15000 20000 25000 10000 15000 20000 25000 10000 15000 20000 25000 + #Training Samples + SFT-DA SFT-RTA SFT-CCI + + Figure 3: Scaling the number of training samples in ICR2 ’s training set. + + LOFT ICR2 ICR2 + SFT-RTA Blocking Context + Methods + Metrics Recall Metrics Recall + NQ HPQA MUSI. NQ HPQA FEV. WoW + NQ 0.6 0.58 0.61 (Ò .01) 0.59 (Ò .01) + SFT-RTA 0.81 0.83 0.37 0.58 0.78 0.64 0.43 HPQA 0.67 0.78 0.68 (Ò .01) 0.78 (“ .00) + SFT-CCI 0.65 0.61 0.26 0.63 0.73 0.69 0.49 FEVER 0.91 0.64 0.91 (“ .00) 0.66 (Ò .02) + RetHead WoW 0.22 0.43 0.21 (Ó .01) 0.46 (Ò .03) + w{Lgen ` Lret 0.01 0.01 0.01 0.36 0.44 0.55 0.51 Avg 0.6 0.61 0.6 (“ .00) 0.62 (Ò .01) + w{Lgen - - - 0.00 0.00 0.00 0.02 + w{Lret - - - 0.33 0.49 0.51 0.49 Table 5: SFT-RTA’s performance before and after block- + RAP Decoding ing the contextual knowledge base with attention mask. + Vanilla RAG 0.19 0.25 0.10 0.36 0.13 0.00 0.00 + SFT-DA 0.95 0.70 0.27 0.75 0.67 0.60 0.69 + SFT-RTA 0.95 0.96 0.39 0.61 0.85 0.60 0.41 an improved model performance. In particular, a + smaller amount of training data fares worse with +Table 4: Retrieval performance measured by recall rate +for various methods using Mistral-2-7B. the retrieve-then-generate approaches, as they are + by nature more challenging to learn compared to +not comparable with the SFT variants. the SFT-DA approach, where answer is directly + generated without an explicit retrieval step. +5.2 In-Context Retrieval Performance +Table 4 reports the recall rates for all methods, 6.2 Blocking Context Attention in +meaning how likely the models retrieve the relevant Retrieve-then-generate Model +provenances. For the SFT variants, we evaluate + To verify if models actually learn to generate the fi- +the retrieval predictions produced in the retrieval + nal responses only from the retrieval predictions in +phase. For the joint retrieval head training approach + our retrieve-then-generate methods (Sec. 4.1), addi- +(RetHead), we analyze the predictions from the + tional experiments were performed where we block +retrieval head. For RAP decoding, we assess the + attention to be paid onto the context beyond the +passages identified by the selected attention heads. + retrieval predictions at the generation step. Table 5 + We find a strong correlation between a model’s + reports the impact of the blocking on the SFT-RTA +recall rate and its downstream task performance, + variant. We observe the performance essentially +highlighting the importance of in-context retrieval + stays the same, indicating the model indeed learned +ability for LCLMs. Our best approach, SFT-RTA + to generate largely on the retrieved passages, ignor- ++ RAP, achieves the highest recall. In contrast, + ing the original context. +Vanilla RAG + RAP exhibits poor retrieval perfor- +mance, consistent with its limited improvement on 6.3 Effect on Attention Heads of +the downstream tasks. The near-random retrieval Retrieve-then-Generate Fine-tuning +performance of joint training of retrieval head in +LOFT explains its failure of generalization. To understand if fine-tuning sharpens attention + heads’ focus on relevant passages, we compare the +6 Discussion hit rates (Sec. 4.2) achieved by the attention heads + between Vanilla RAG and all of our SFT variants +6.1 Scaling the Supervised Fine-tuning (Sec. 4.1), and the results are shown in Figure 4. +We are also interested in how the performance of Similar to (Wu et al., 2024), we find that a small +the proposed SFT variants scale with the training group of attention heads can obtain higher hit rates +set size. We train the three variants, SFT-DA, SFT- than the others. However, unlike Vanilla RAG, +RTA, and SFT-CCI, with the same 10K, 15K and SFT methods produce more retrieval-focused atten- +25K examples from ICR2 ’s training set, and re- tion heads, and achieve higher peak hit rates. This +port their performance on each task in the ICR2 demonstrates the effectiveness of our SFT meth- +benchmark, as shown in Fig 3. We observe that ods and our curated ICR2 training set in enhancing +an increased training set size in general leads to LCLMs’ performance of in-context retrieval. + Vanilla RAG SFT-DA SFT-RTA SFT-CCI + 0.75 SFT-DA SFT-DA SFT-DA Benchmark SFT-DA SFT-DA + RAP + 0.50 + NQ + + 0.25 Vanilla RAG Vanilla RAG Vanilla RAG NQ 4.23 4.99 (Ò .76) + 0.00 + HotpotQA 4.19 4.81 (Ò .62) + 0 200 400 0 500 0 500 0 200 MUSIQUE 4.24 5.05 (Ò .81) + 0.75 SFT-DA SFT-DA SFT-DA + HotpotQA + + + + + 0.50 Table 6: Latency for SFT-DA with and without the RAP + Vanilla RAG Vanilla RAG Vanilla RAG + 0.25 + decoding. Time is measured in seconds per query. + 0.00 + 0 200 0 250 500 0 500 0 200 + section, we apply different value settings when de- + 0.75 + 0.50 ploying the SFT-RTA + RAP combined approach + FEVER + + + + + Vanilla RAG Vanilla RAG Vanilla RAG + 0.25 SFT-DA SFT-DA SFT-DA to explore their effect on model performance. + 0.00 + 0 200 400 0 100 0 500 0 200 Figure 5 shows the high-level results on both + 0.75 SFT-DA SFT-DA SFT-DA LOFT and ICR2 (Appendix E has more details). + 0.50 + As expected, we observe in the middle column that + WoW + + + + + 0.25 + Vanilla RAG Vanilla RAG Vanilla RAG + 0.00 an increasing M or Q will increase the recall rate as + 0 200 400 0 200 0 200 400 0 200 + Retrieval Head ID Retrieval Head ID Retrieval Head ID Retrieval Head ID the resulting larger pools of selected passages will + more likely include the relevant ones. We also find +Figure 4: Attention heads with above-zero hit rates. SFT +produces more retrieval attention heads. Retrieve-then- that increasing M as opposed to Q is more effective +generate training activates a higher peak of hit rate. in improving the recall rate as certain task such as + ICR2 + ICR2-Metrics ICR2-Recall-RAP ICR2-Recall-RTA + HotpotQA requires multi-hop retrieval. However, + 0.60 0.58 0.59 0.76 0.82 0.88 0.61 0.59 0.60 the increased recall rate does not always translate to + M=8 M=4 M=2 M=1 + + + + + M=8 M=4 M=2 M=1 + + + + + M=8 M=4 M=2 M=1 + + + + + 0.61 0.59 0.59 0.84 0.94 0.95 0.60 0.63 0.61 a better performance (left column) or RTA’s recall + 0.57 0.58 0.58 0.92 0.97 0.99 0.60 0.59 0.60 rate , as a higher M or Q may also introduce more + 0.59 0.58 0.58 0.96 0.99 1.00 0.60 0.59 0.60 confounders. This suggests the further reduction in + Q=2 Q=4 Q=8 Q=2 Q=4 + LOFT + Q=8 Q=2 Q=4 Q=8 confounding effects is still an opening future work. + LOFT-Metrics LOFT-Recall-RAP LOFT-Recall-RTA + 0.62 0.61 0.67 0.84 0.88 0.93 0.75 0.76 0.76 6.5 Decoding Speed + M=8 M=4 M=2 M=1 + + + + + M=8 M=4 M=2 M=1 + + + + + M=8 M=4 M=2 M=1 + + + + + 0.64 0.60 0.61 0.91 0.96 0.98 0.77 0.75 0.74 Our final analysis is on the efficiency of RAP. + 0.61 0.62 0.63 0.96 0.99 1.00 0.74 0.71 0.71 Based on the SFT-DA variant, we report its latency + 0.63 0.57 0.57 0.99 1.00 1.00 0.75 0.66 0.67 with and without the RAP enhancement in Table 6. + Q=2 Q=4 Q=8 Q=2 Q=4 Q=8 Q=2 Q=4 Q=8 We find that the RAP decoding does not increase +Figure 5: Effect of adjusting Q (number of attention latency significantly, despite it adds one additional +heads for retrieval) and M (number of passages to se- decoding step to the base method. This can be at- +lect) when applying SFT-RTA + RAP. Left column: av- tributed to the much shorter context retrieved by +erage model performance measured by the tasks-specific the attention heads, thus avoiding expensive long- +metrics. Middle column: attention heads’ retrieval mea- context computation as in the baseline. This in- +sured by the recall rate. Right column: RTA’s retrieval + crease can be further reduced with approaches such +measured by the exact match. + as KV caching, which we leave for future work. + Among the three SFT variants, we find that SFT- 7 Conclusion +RTA in general achieves higher peak hit rates and +activates more attention heads for retrieval. In par- In this paper, we introduce ICR2 , a new bench- +ticular, SFT-CCI is not as effective in recruiting mark designed as a more discriminative benchmark +as many attention heads, possibly because context for evaluating LCLMs in in-context retrieval and +IDs themselves are not informative enough for the reasoning. Our findings highlight the limitations +model to learn the retrieval task well. We also note for the current models. We propose three methods +that SFT-DA fares a lot worse on FEVER, possibly —retrieve-then-generate fine-tuning, retrieval atten- +due to the lack of chain-of-thought style of assist. tion probing, and joint retrieval head training — to + enhance models, achieving the results comparable +6.4 Retrieval Attention Probing to GPT-4 with a smaller model footprint. +Our inference-time method RAP (Sec. 4.2) uses + Ethical Considerations +two hyperparameters: Q is the number of the at- +tention heads we recruit for retrieval, and M is the We do not expect any ethical concerns to be raised +number of passages each head retrieves. In this with respect to this work. + Limitations Andrew McNamara, Bhaskar Mitra, Tri Nguyen, + et al. 2016. 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Qwen2 + technical report. arXiv preprint arXiv:2407.10671. + A Statistics for LOFT and ICR2 D Retrieval Performance for Re-ranking + Strategies + Task #CTX #Tokens #Prov Table 10 presents a comprehensive evaluation of + NQ 215 28,911 1 different re-ranking strategies applied to retrieval- + LOFT HPQA 276 28,924 1.98 augmented generation (RAG) across two bench- + MUSIQ. 207 28,806 1.67 + mark suites: LOFT (comprising NQ, HPQA, and + NQ 202 20,441 1 MUSI.) and ICR2 (including NQ, HPQA, FEV, and + 2 HPQA 202 20,818 2.09 + ICR + FEVER 202 21,136 1 WoW). We compare baseline RAG without re- + WoW 202 21,068 1 ranking, re-ranking using lightweight models (Tiny- + BERT and MiniLM) at varying retrieval depths +Table 7: Statistics for LOFT and our dataset ICR2 : k P t8, 32, 50u, and our best-performing variant +#CTX is to the average number of passages per query in (SFT-RTA w/ RAP). Results show consistent im- +the contextual knowledge base, #Tokens is the average + provements with increased k, and MiniLM-based +length of the CiC prompt, and #Prov. is the average +number of provenance (positive passages) per query. re-rankers often slightly outperform TinyBERT + counterparts. Notably, the SFT-RTA w/ RAP + We present the comparison in dataset statistics model yields the highest performance across most +between LOFT and ICR2 in Table 7. datasets, particularly excelling in more challenging + ICR2 domains. These findings suggest the strong re- +B Prompt Template for the trieval capabilities of our SFT-RTA approach com- + Retrieval-augmented Generation pared to the traditional re-ranking strategies. + +We show the Corpus-in-Context (CiC; Lee et al. E Detailed Results for RAP Decoding +2024) prompt template used in our experiments in + We show the detailed results for each task of LOFT +Table 8. + and ICR2 in Figure 6 and 7. +C Generalization to Other LCLMs F Details of Experiment Setup +Table 9 presents the performance of the LLaMA-3- F.1 Training Details +Instruct-8B model across two benchmarks—NQ- For all models, we set the base value of the ro- +LOFT and HPQA-LOFT—evaluated under LOFT tary position embedding to 1e6 following Su et al. +and ICR2 . The results highlight several key trends. (2024). Training is conducted with a batch size of +First, the baseline Closebook performs poorly, par- 1 per Nvidia A100-40G GPU, and gradients are ac- +ticularly on the HPQA dataset, underscoring the cumulated every 4 steps. Each model is trained for +need for external information. Incorporating re- up to 10,000 steps (approximately 2 epochs), with +trieval via RAG offers significant improvements, the best-performing checkpoint on the validation +especially on HPQA, where performance increases set selected as part of an early-stopping strategy. +from 0.19 to 0.56. The Oracle setting represents The learning rate is set to 1e ´ 5, and the maxi- +the upper bound of retrieval quality, and shows the mum sequence length during training is capped at +strongest performance overall. 32,768 tokens, discarding any sequences exceeding + We observe that adding a reranker such as Tiny- this threshold. +BERT to RAG further improves results under +the LOFT setup, reaching 0.89 on both NQ and F.2 Inference Parameters +HPQA—approaching Oracle-level performance. During inference, we use greedy decoding for all +Among supervised fine-tuning methods, SFT-DA models, allowing a maximum generated sequence +and SFT-RTA provide moderate improvements over length of 1,024 tokens. For RAP decoding, 100 ran- +vanilla RAG. Notably, integrating our proposed dom instances from the validation set are used to +RAP mechanism (e.g., in SFT-DA w/ RAP) leads probe the attention heads responsible for retrieval. +to consistent gains, especially under ICR2 . For For SFT-DA, retrieval is performed using the desig- +example, SFT-DA w/ RAP achieves the best NQ nated retrieval attention heads based on the first and +score (0.61), validating the effectiveness of RAP only decoded token. For SFT-RTA, retrieval is per- +in enhancing decision quality during retrieval and formed using all tokens generated in the retrieval +answer generation. step to ensure complete context coverage. + Task Prompt Template + [INST] Please answer the following question given the following passages: + {Corpus} + QA + + + Question: {Query} + Answer: [/INST] + Fact Verification + + + + + [INST] According to the following passages, please verify the given claim + and predict your judgment on its factuality as TRUE or FALSE: + {Corpus} + Claim: {Query} + Judgement: [/INST] + [INST] According to the given passages, please provide a single response to + Dialogue Completion + + + + + complete the following conversation by role-playing as either Person A or + Person B. Your response should be as knowledgeable and coherent with the + conversation history as possible: + {Corpus} + Conversation: {Query} + [/INST] + +Table 8: Prompt template used for RAG tasks in our experiments. {Corpus} refers to the provided contextual +knowledge base, and {Query} refers to a query in ICR2 or LOFT. A {Query} can be a question in the question +answering tasks (Kwiatkowski et al., 2019; Yang et al., 2018), a claim to be verified in the fact verification task +(Thorne et al., 2018), or a conversation history in the dialogue completion task (Dinan et al., 2018). + + + LOFT ICR2 • HotpotQA: Q “ 4, M “ 4 + LLaMA-3-Instruct + NQ HPQA NQ HPQA + Closebook 0.41 0.19 0.44 0.19 • WoW: Q “ 8, M “ 2 + RAG 0.45 0.56 0.53 0.45 + RAG w/ TinyBERT 0.89 0.89 0.55 0.48 For SFT-RTA on ICR2 : + Oracle 0.81 0.77 0.80 0.74 + SFT-DA 0.80 0.60 0.51 0.52 + SFT-RTA 0.84 0.63 0.51 0.57 • NaturalQuestions: Q “ 2, M “ 1 + SFT-DA w/ RAP 0.83 0.72 0.61 0.60 + • HotpotQA: Q “ 2, M “ 1 +Table 9: LLaMA-3’s performances on NQ and Hot- +potQA for LOFT and ICR2 with our methods on NQ- • FEVER: Q “ 2, M “ 8 +LOFT and HPQA-LOFT. + + • WoW: Q “ 4, M “ 8 +F.2.1 RAP Hyperparameter Settings +In this section, we detail the hyperparameters used For SFT-RTA on LOFT: +in applying RAP decoding to SFT-DA and SFT- +RTA on ICR2 and LOFT. • NaturalQuestions: Q “ 8, M “ 4 + For SFT-DA on ICR2 : + • HotpotQA: Q “ 4, M “ 2 + • NaturalQuestions: Q “ 4, M “ 1 + + • HotpotQA: Q “ 8, M “ 1 • WoW: Q “ 8, M “ 2 + + • FEVER: Q “ 4, M “ 4 G Prompt Templates for Supervised + Fine-tuning + • WoW: Q “ 4, M “ 4 + + For SFT-DA on LOFT: We visualize three templates used for super- + vised fine-tuning LCLMs to perform retrieve-then- + • NaturalQuestions: Q “ 4, M “ 4 generate generation in Figure 8. + LOFT ICR2 + Re-ranker Model K + NQ HPQA MUSI. NQ HPQA FEV WoW + TinyBERT 8 0.99 0.98 0.80 0.61 0.65 0.68 0.35 + MiniLM 8 1.00 0.98 0.82 0.66 0.65 0.70 0.27 + TinyBERT 32 0.99 0.99 0.81 0.89 0.82 0.87 0.55 + MiniLM 32 1.00 1.00 0.90 0.86 0.81 0.89 0.43 + TinyBERT 50 0.99 0.99 0.82 0.91 0.84 0.92 0.60 + MiniLM 50 1.00 1.00 0.92 0.91 0.88 0.91 0.50 + SFT-RTA w/ RAP / 0.95 0.99 1.00 0.90 0.95 0.97 0.92 + + Table 10: Retrieval performance comparison of re-ranker models across LOFT and ICR2 benchmarks. + + LOFT-NQ + LOFT-Metrics LOFT-Recall-RAP LOFT-Recall-RTA + 0.85 0.83 0.82 0.95 0.97 0.99 0.92 0.93 0.90 + M=8 M=4 M=2 M=1 + + + + + M=8 M=4 M=2 M=1 + + + + + M=8 M=4 M=2 M=1 + 0.80 0.80 0.82 0.97 1.00 1.00 0.95 0.93 0.92 + + 0.81 0.78 0.78 0.99 1.00 1.00 0.94 0.85 0.87 + + 0.84 0.73 0.75 1.00 1.00 1.00 0.93 0.81 0.80 + + Q=2 Q=4 Q=8 Q=2 Q=4 Q=8 Q=2 Q=4 Q=8 + + LOFT-HotpotQA + LOFT-Metrics LOFT-Recall-RAP LOFT-Recall-RTA + 0.79 0.77 0.81 0.94 0.98 0.99 0.96 0.95 0.95 + M=8 M=4 M=2 M=1 + + + + + M=8 M=4 M=2 M=1 + + + + + M=8 M=4 M=2 M=1 + + + + + 0.77 0.75 0.76 0.98 0.99 1.00 0.95 0.95 0.93 + + 0.73 0.76 0.71 0.99 1.00 1.00 0.91 0.96 0.88 + + 0.73 0.68 0.68 1.00 1.00 1.00 0.90 0.85 0.85 + + Q=2 Q=4 Q=8 Q=2 Q=4 Q=8 Q=2 Q=4 Q=8 + + LOFT-MUSIQUE + LOFT-Metrics LOFT-Recall-RAP LOFT-Recall-RTA + 0.23 0.23 0.37 0.62 0.69 0.81 0.38 0.39 0.44 + M=8 M=4 M=2 M=1 + + + + + M=8 M=4 M=2 M=1 + + + + + M=8 M=4 M=2 M=1 + + + + + 0.31 0.24 0.25 0.79 0.89 0.94 0.40 0.38 0.37 + + 0.29 0.32 0.39 0.90 0.96 1.00 0.36 0.33 0.39 + + 0.33 0.31 0.27 0.96 1.00 1.00 0.41 0.33 0.37 + + Q=2 Q=4 Q=8 Q=2 Q=4 Q=8 Q=2 Q=4 Q=8 + + + +Figure 6: Effect of adjusting Q (number of attention heads for retrieval) and M (number of passages to select) +when applying SFT-RTA + RAP on all tasks in LOFT. Left column: average model performance measured by the +tasks-specific metrics. Middle column: retrieval performance measured by the recall rate. Right column: retrieval +performance measured by the exact match. + + +H Visualization for Joint Retrieval Head I Effect of Retrieval Delineation + Training + As outlined in Section 4.1, our retrieve-then- +We visualize the architecture for adding a retrieval generate variants, SFT-RTA and SFT-CCI, utilize +head jointly trained with language generation in special symbols and +Figure 9 for easing the understanding of the ap- to explicitly mark the boundaries of the retrieval +proach. step. To examine the impact of these boundary to- + ICR2-NQ + ICR2-Metrics ICR2-Recall-RAP ICR2-Recall-RTA + 0.61 0.56 0.59 0.80 0.85 0.89 0.58 0.60 0.60 + + + + + M=8 M=4 M=2 M=1 + + + + + M=8 M=4 M=2 M=1 + + + + + M=8 M=4 M=2 M=1 + 0.63 0.61 0.61 0.90 0.96 0.97 0.61 0.65 0.59 + + 0.54 0.58 0.56 0.96 1.00 1.00 0.60 0.59 0.57 + + 0.62 0.61 0.56 0.97 1.00 1.00 0.61 0.57 0.55 + + Q=2 Q=4 Q=8 Q=2 Q=4 Q=8 Q=2 Q=4 Q=8 + + ICR2-HotpotQA + ICR2-Metrics ICR2-Recall-RAP ICR2-Recall-RTA + 0.71 0.65 0.67 0.93 0.92 0.99 0.85 0.75 0.81 + M=8 M=4 M=2 M=1 + + + + + M=8 M=4 M=2 M=1 + + + + + M=8 M=4 M=2 M=1 + 0.71 0.65 0.64 0.95 0.96 1.00 0.80 0.81 0.78 + + 0.62 0.64 0.62 0.99 1.00 1.00 0.77 0.77 0.77 + + 0.59 0.59 0.64 1.00 1.00 1.00 0.78 0.77 0.79 + + Q=2 Q=4 Q=8 Q=2 Q=4 Q=8 Q=2 Q=4 Q=8 + + ICR2-FEVER + ICR2-Metrics ICR2-Recall-RAP ICR2-Recall-RTA + 0.88 0.90 0.90 0.82 0.87 0.93 0.59 0.59 0.60 + M=8 M=4 M=2 M=1 + + + + + M=8 M=4 M=2 M=1 + + + + + M=8 M=4 M=2 M=1 + 0.89 0.89 0.91 0.87 0.96 0.96 0.60 0.66 0.65 + + 0.89 0.90 0.91 0.93 0.99 1.00 0.60 0.61 0.64 + + 0.92 0.91 0.90 0.97 1.00 1.00 0.60 0.64 0.59 + + Q=2 Q=4 Q=8 Q=2 Q=4 Q=8 Q=2 Q=4 Q=8 + + ICR2-WoW + ICR2-Metrics ICR2-Recall-RAP ICR2-Recall-RTA + 0.21 0.22 0.21 0.50 0.66 0.71 0.42 0.41 0.40 + M=8 M=4 M=2 M=1 + + + + + M=8 M=4 M=2 M=1 + + + + + M=8 M=4 M=2 M=1 + + + + + 0.21 0.22 0.22 0.66 0.86 0.88 0.40 0.41 0.44 + + 0.21 0.22 0.22 0.79 0.91 0.95 0.42 0.41 0.43 + + 0.23 0.20 0.21 0.92 0.98 0.99 0.41 0.37 0.46 + + Q=2 Q=4 Q=8 Q=2 Q=4 Q=8 Q=2 Q=4 Q=8 + + + +Figure 7: Effect of adjusting Q (number of attention heads for retrieval) and M (number of passages to select) +when applying SFT-RTA + RAP on all tasks in ICR2 . Left column: average model performance measured by the +tasks-specific metrics. Middle column: retrieval performance measured by the recall rate. Right column: retrieval +performance measured by the exact match. + + + Benchmark SFT-RTA w{o SFT-CCI w{o + kens, we conducted an ablation study. As shown in + NQ 0.74 0.7 (Ó .04) 0.76 0.63 (Ó .13) + Table 11, removing these tokens in general leads to + LOFT + + + + + HPQA 0.69 0.66 (Ó .03) 0.54 0.58 (Ò .04) a decline in model performance. This suggests that + MUSI. 0.33 0.24 (Ó .09) 0.35 0.24 (Ó .11) clearly delineating the retrieval step is crucial for + NQ 0.60 0.56 (Ó .04) 0.63 0.61 (Ó .02) the model to learn effectively. + ICR2 + + + + + HPQA 0.67 0.7 (Ó .03) 0.63 0.65 (Ò .02) + FEVER 0.91 0.92 (Ò .01) 0.89 0.86 (Ó .03) + WoW 0.22 0.20 (Ó .02) 0.21 0.22 (Ò .01) + + +Table 11: Effect of removing the special boundary to- +kens for the retrieve-then-generate variants. + Direct Answer: The answer is + Input: Answer the February 14, 2018. + question based on the + provided corpus. + LCLM + Retrieve-then-Answer: The + relevant passage for the + Corpus: { Contextual Knowledge Base question is, “Barry was born + PSG001: {p_1} in February 14, 2018.” The + correct answer is February + PSG002: {p_2} + 14, 2018. + … + } + Cite-Context-ID: The + relevant passage ID for this + Query: When is Barry’s question is PSG004. The + birthday? correct answer is February + 14, 2018. + + + Figure 8: Templates for Direct Answer (DA), Retrieve-Then-Answer (RTA) and Cite-Context-ID (CCI). + + + + + Gumbel 10010 + Retrieval Head Top-K + 01101 + + + + + LCLM LCLM + Contextual Knowledge Base Contextual Knowledge Base + + + + + Query: When is Barry’s Direct Answer: The answer is + birthday? February 14, 2018. + + + + +Figure 9: A retrieval head is added to predict the Top- +K context for generating final responses. The head is +jointly optimized with the generation head. + \ No newline at end of file diff --git a/wizard-work/sphinx/archive-longctx-retrieval-2026-10-26/text/2503.14800v3.txt b/wizard-work/sphinx/archive-longctx-retrieval-2026-10-26/text/2503.14800v3.txt new file mode 100644 index 0000000..8d1480d --- /dev/null +++ b/wizard-work/sphinx/archive-longctx-retrieval-2026-10-26/text/2503.14800v3.txt @@ -0,0 +1,940 @@ + Long Context Modeling with Ranked Memory-Augmented Retrieval + Ghadir Alselwi1 , Hao Xue1,2,3 , Shoaib Jameel4 , Basem Suleiman1 , + Flora D. Salim1 , Imran Razzak5,1 + 1 + University of New South Wales, Sydney, NSW, Australia + 2 + Hong Kong University of Science and Technology (Guangzhou) + 3 + The Hong Kong University of Science and Technology + 4 + University of Southampton, Southampton, UK + 5 + Mohamed Bin Zayed University of Artificial Intelligence, Abu Dhabi, UAE + {g.alselwi,b.suleiman,flora.salim}@unsw.edu.au + haoxue@hkust-gz.edu.cn, M.S.Jameel@southampton.ac.uk, imran.razzak@mbzuai.ac.ae + Abstract ory bank model is a large, non-trainable store + of past context representations. Instead of re- + Effective long-term memory management is +arXiv:2503.14800v3 [cs.IR] 18 May 2026 + + + + + computing representations for all past tokens every + crucial for language models handling extended + contexts. We introduce the Enhanced Ranked time, these representations are pre-computed and + Memory Augmented Retrieval (ERMAR) stored. Given the current context, MemLong re- + framework, which dynamically ranks mem- trieves relevant segments from the memory bank. + ory entries based on relevance. Unlike prior It uses a dot product similarity search to find the + models, ERMAR employs a novel relevance memory entries most related to the current context. + scoring mechanism and a pointwise re-ranking This allows the model to focus only on the most + model for key-value embeddings, inspired pertinent past information, rather than processing + by learning-to-rank techniques in informa- + the entire history. However, its treatment of all + tion retrieval. By integrating historical us- + age patterns and adaptive retrieval, ERMAR key-value (K-V) pairs with equal weight, regard- + achieves state-of-the-art results on standard less of their contextual relevance, often leads to + benchmarks, demonstrating superior scalabil- information overload and reduced retrieval preci- + ity and performance in long-context tasks. sion. This limitation becomes particularly evident + Code will be released at https://github. in scenarios requiring context management. + com/cruiseresearchgroup/ERMAR. + We have developed a novel model that addresses + 1 Introduction the aforementioned limitations by building upon + Memlong (Liu et al., 2024), a publicly available + Large Language Models (LLMs) face a fundamen- baseline on GitHub1 . Our Enhanced Ranked + tal limitation in processing long-context scenarios Memory Augmented Retrieval (ERMAR) model + due to the quadratic complexity of attention mecha- has a novel relevance scoring mechanism that fun- + nisms and increasing memory demands during gen- damentally improves context retrieval and utiliza- + eration (Vaswani, 2017; Tworkowski et al., 2024). tion for K-V embeddings. Unlike MemLong, ER- + Consider a scenario in an automated customer ser- MAR employs multiplication (Cao et al., 2007) + vice system: A customer reports an issue with their to compute relevance scores, enabling a more nu- + printer, referencing a setup process from a previous anced and context-aware assessment of semantic + conversation that occurred two hours ago. After alignment between queries and stored memory. ER- + 50 messages of troubleshooting, the customer men- MAR also incorporates a re-ranking mechanism + tions that the same error from the beginning has that dynamically reorders K-V embeddings based + resurfaced. Traditional LLMs, constrained by their on their relevance scores, ensuring that the most + context window, would struggle to access the cru- pertinent information is prioritized during retrieval. + cial earlier context about the initial setup process, This re-ranking process, combined with an adaptive + leading to inconsistent or incomplete responses, retrieval system that integrates historical usage pat- + Figure 1. It is well known that handling extended terns, allows ERMAR to capture subtle contextual + contexts remains a significant challenge, particu- relationships better and refine memory prioritiza- + larly in applications requiring document analysis tion. As shown in Figure 1, ERMAR processes + and sustained dialogue interactions. incoming queries and long-context conversations + The recent MemLong (Liu et al., 2024) architec- through a novel ranking architecture, employing + ture stores and accesses historical context through + 1 + basic chunk-level memory operations. The mem- https://github.com/Bui1dMySea/MemLong + K-V pairs ranking (K0 -V0 , K1 -V1 ,...,Ki -Vi ) and cor- Longformer (Beltagy et al., 2020) and BigBird (Za- +responding embeddings to perform semantic search heer et al., 2020) reduce computational complexity +and ranking of relevant historical information. while maintaining model capabilities through selec- + Our novel ERMAR model introduces three key tive attention patterns. Position encoding adapta- +improvements: (i) A semantic similarity metric tions like RoPE (Su et al., 2024) and ALiBi (Press +to measure contextual alignment between query et al., 2022) have enhanced models’ ability to han- +embeddings and key-value pairs; (ii) A weighted dle longer sequences. YARN (Peng et al., 2023) +scoring function that considers content similarity further advanced this through dynamic position em- +and contextual relevance; and (iii) Integration of beddings, demonstrating reliable generalization up +historical usage patterns to refine relevance assess- to 128k tokens. +ment. + 2.3 Learning-to-Rank and Information +2 Related Work Retrieval + Our ERMAR framework draws inspiration from +The challenge of enabling language models to ef- learning-to-rank techniques in information retrieval. +fectively process extended contexts has driven re- Traditional ranking approaches like BM25 struggle +search across multiple domains. We organize re- with semantic similarity, leading to neural ranking +lated work into key areas that inform our ERMAR models using learned representations. Pointwise +framework. ranking approaches (Cao et al., 2007) predict rel- + evance scores for query-document pairs, directly +2.1 Memory-Augmented Neural Networks + inspiring our relevance scoring mechanism. Dense +Early architectures like Neural Turing Ma- retrieval methods like DPR (Karpukhin et al., 2020) +chines (Graves et al., 2014) and Differentiable Neu- and ColBERT (Khattab and Zaharia, 2020) demon- +ral Computers (Graves et al., 2016) introduced ex- strate the effectiveness of learned dense represen- +ternal memory beyond model parameters. Recent tations for ranking through similarity scores—a +models for long-context language modeling include paradigm we adapt for memory entry ranking. +RetroMAE (Xiao et al., 2022), which struggled BERT-based re-ranking models (Nogueira and Cho, +with semantic coherence, and Memorizing Trans- 2019) show that sophisticated re-ranking signifi- +formers (Wu et al., 2022), which improved cross- cantly improves retrieval quality. This two-stage +sequence information storage but faced scalability retrieve-then-rerank paradigm directly influences +limits. MemLong (Liu et al., 2024) stores histor- our design, where we first retrieve candidate mem- +ical context via chunk-level memory operations, ory entries and then apply learned re-ranking. +but its uniform treatment of key-value pairs can +cause information overload and reduced retrieval 2.4 Current Limitations and Gaps +precision. Despite significant progress, current approaches + face limitations that motivate ERMAR: +2.2 Retrieval-Augmented Generation and Static Memory Management: Most ap- + Long-Context Modeling proaches use fixed memory structures that do not +Retrieval-augmented generation (RAG)(Lewis adapt to content importance or usage patterns, lead- +et al., 2020) pioneered integrating dense passage ing to inefficient memory utilization. +retrieval with generative models, while Fusion-in- Uniform Memory Treatment: Existing meth- +Decoder (FiD)(Izacard and Grave, 2020) improved ods treat all memory entries equally, lacking mech- +efficiency through independent passage process- anisms to prioritize more relevant information. +ing. REALM (Guu et al., 2020) introduced end- Limited Semantic Understanding: Memory +to-end learning of retrieval and generation, and systems often rely on simple similarity metrics +DPR (Karpukhin et al., 2020) established dense without considering contextual nuance or temporal +passage retrieval standards. However, these meth- relevance. +ods typically focus on static corpora rather than Scalability Trade-offs: Current approaches face +dynamic context-aware memory management. The difficult trade-offs between memory capacity, re- +quadratic complexity of attention mechanisms has trieval accuracy, and computational efficiency. +driven research into efficient long-context archi- Our ERMAR framework addresses these limita- +tectures. Sparse attention mechanisms such as tions through dynamic relevance scoring, adaptive + Figure 1: Motivating example of the ERMAR retrieval workflow. Left: A long customer support conversation where +the current query references information from much earlier context. Right: The four-stage workflow — ⃝ 1 Long +Memory Retrieval, ⃝ 2 Search, ⃝3 Reranking, ⃝ 4 Memory Fusion Generation. The ERMAR output (top) correctly +references earlier context, whereas the output without reranking (bottom) produces a generic response. + + +memory management, and sophisticated re-ranking the model’s output on the most relevant historical +mechanisms inspired by information retrieval tech- context. +niques, providing a more principled approach to This design directly addresses the information +long-context memory management. dilution problem in long contexts: without rerank- + ing (bottom of Figure 1), the model produces a +3 Our Novel ERMAR Model generic response that fails to reference the earlier +Figure 1 presents a concrete motivating example of printer setup context; with reranking (ERMAR out- +how ERMAR’s contextual ranking mechanism op- put, top), the model correctly retrieves and incor- +erates in practice, while Figure 2 illustrates the porates the relevant earlier information. +overall architecture of the proposed framework. As shown in Figure 2, the ERMAR architec- +As shown in Figure 1, ERMAR processes a long- ture consists of three interacting components: a +context query through four sequential stages. First, frozen lower transformer block, a trainable up- +the Long Memory Retrieval module encodes the per block with retrieval-augmented attention, and +full input history into chunk-level key–value (KV) a memory retrieval module with reranking. Input +pairs stored in a memory bank, alongside embed- text is processed by the frozen lower block (Causal +dings that capture semantic representations of each Attention and Feed Forward layers), whose hid- +chunk. Second, when a new query arrives, its den states form the key–value pairs {(Kj , Vj )} ∈ +embedding is used to search the memory bank Rdmodel stored in the Memory Bank. In parallel, +for candidate KV pairs via similarity matching. the Dense Embedder independently encodes the +Third, the retrieved candidates pass through the same input into chunk-level retrieval embeddings +Reranking module — the core novelty over Mem- E1 , . . . , Ei ∈ Rdret , which serve as the semantic +Long — which assigns relevance scores to each KV index for similarity-based retrieval. The Rerank- +pair and re-orders them by contextual importance, ing module then scores all stored embeddings and +elevating high-relevance entries (K6 -V6 , ranked) KV pairs using Equation 2, re-ordering them by +above lower-relevance ones (K0 -V0 , unranked). Fi- relevance to the current query and isolating the +nally, only the top-ranked KV pairs are passed to top-ranked subset (right stacks in Figure 2). The +Memory Fusion Generation, which conditions Retrieval module performs top-K selection over + with a relevance scoring mechanism, + + M : |Rd{z }×R + model dmodel + | {z } × R + dret + |{z} → S , (1) + keys values embeddings + + + where dmodel denotes the transformer hidden di- + mension, dret the retrieval embedding dimension, + and S the space of scored memory items (see Ap- + pendix A.8 for embedding dimensionality analy- + sis). + Relevance Score. Given a query embedding + q ∈ Rdret and a matrix of key embeddings K = + [k1 , . . . , km ] ∈ Rm×dret , where each row ki ∈ Rdret + corresponds to a key embedding, the relevance +Figure 2: Architecture of the ERMAR model. The score is defined as: +frozen lower block (blue dashed, snowflake) produces + qK⊤ +   +hidden states stored as key–value pairs (dmodel ) in the α(q, K) = softmax √ , (2) + dret +Memory Bank, while the Dense Embedder encodes the +input into chunk-level retrieval embeddings (dret ). The √ + where dret normalizes the similarity scores to pre- +Reranking module scores and ranks all stored entries, +and the Retrieval module injects the top-K ranked pairs + vent excessively large values. +into the trainable upper block via Retrieval Causal The resulting vector α(q, K) represents a prob- +Attention (orange arrow) to condition the final out- ability distribution over the keys, where each en- +put. Frozen and trainable components are marked by try αi denotes the relative importance (attention +snowflake and flame icons respectively. weight) of the i-th key with respect to the query q. + The final + Pm context or weighted output is obtained as + o = i=1 αi Vi , where Vi denotes the correspond- + ing value embedding associated with key ki . +this ranked subset and injects the selected KV pairs + We now introduce the Relevance Scoring with +into the trainable upper block via Retrieval Causal + Adaptive Retrieval (RSAR) mechanism, which +Attention (orange arrow), producing a context- + dynamically ranks memory entries according to +enriched representation from which the final output + their importance to the current query, thereby en- +is generated. + hancing the retrieval process. The relevance score + ERMAR maintains consistency through frozen α(q, K), as defined in Equation 2, is employed to +lower layers and selective parameter updates. rank the memory entries. +Specifically, ERMAR (i) stores important infor- RSAR augments the memory module by intro- +mation from earlier parts of the text, (ii) assigns ducing ranked key–value entries, represented as +relevance scores to stored information based on (Kj , Vj , sj ), where sj denotes the relevance score +its importance to the current context, and (iii) re- for each entry. These scores allow the system to pri- +trieves only the most relevant historical information oritize the most informative content during retrieval +when needed. The relevance scoring mechanism while maintaining computational efficiency. To op- +is analogous to the attention operation, enabling timize memory usage, a pruning strategy is applied +the model to focus on salient parts of the mem- to remove less relevant entries. Specifically, entries +ory. There is also a “loose” pointwise connection: with scores below a predefined threshold are dis- +while relevance-based reranking acts as an auxil- carded, preserving only the most critical contextual +iary mechanism that enhances memory retrieval information. +quality, the primary optimization target remains The enhanced retrieval mechanism is defined as: +next-token prediction likelihood. + RRSAR (tq , s) = TopK {sim(E(tq ), e) · maxj sj | e ∈ s}, (3) + Let V be a finite vocabulary, and x = +(x1 , . . . , xn ) ∈ V n be a token sequence with a where tq denotes the query token, E(tq ) represents +preceding context x

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