.
doi: 10.18653/v1/n19-1246.
URL https://aclanthology.org/N19-1246/.
Duan et al. (2024)
Haodong Duan, Junming Yang, Yuxuan Qiao, Xinyu Fang, Lin Chen, Yuan Liu, Xiaoyi
Dong, Yuhang Zang, Pan Zhang, Jiaqi Wang, Dahua Lin, and Kai Chen.
VLMEvalKit: An open-source toolkit for evaluating large
multi-modality models.
In Proceedings of the ACM International Conference on
Multimedia (MM), pp. 11198–11201, 2024.
doi: 10.1145/3664647.3685520.
URL https://dl.acm.org/doi/10.1145/3664647.3685520.
Dubois et al. (2024)
Yann Dubois, Percy Liang, and Tatsunori Hashimoto.
Length-controlled AlpacaEval: A simple debiasing of automatic
evaluators.
In Conference on Language Modeling (COLM), 2024.
URL https://openreview.net/forum?id=CybBmzWBX0.
Durbin (2023)
Jon Durbin.
airoboros-3.1: Instruction tuning data generated with
Self-Instruct, 2023.
URL https://huggingface.co/datasets/jondurbin/airoboros-3.1.
Fan et al. (2026)
Yu Fan, Jingwei Ni, Jakob Merane, Yang Tian, Yoan Hermstrüwer, Yinya Huang,
Mubashara Akhtar, Etienne Salimbeni, Florian Geering, Oliver Dreyer, Daniel
Brunner, Markus Leippold, Mrinmaya Sachan, Alexander Stremitzer, Christoph
Engel, et al.
LEXam: Benchmarking legal reasoning on 340 law exams.
In International Conference on Learning Representations
(ICLR), pp. 43451–43489, 2026.
URL
https://proceedings.iclr.cc/paper_files/paper/2026/hash/47cbdfce2fc71cd473bd7d409cd1bf4b-Abstract-Conference.html.
Fu et al. (2026)
Tingchen Fu, Yafu Li, Jiawei Gu, Xiaoye Qu, and Yu Cheng.
Scaling reasoning, losing control: Evaluating instruction following
in large reasoning models.
In Proceedings of the Annual Meeting of the Association for
Computational Linguistics (ACL), pp. 40445–40463, 2026.
doi: 10.18653/v1/2026.acl-long.1878.
URL https://aclanthology.org/2026.acl-long.1878/.
Gameiro et al. (2024)
Henrique Da Silva Gameiro, Andrei Kucharavy, and Ljiljana Dolamic.
LLM detectors still fall short of real world: Case of
LLM-generated short news-like posts, 2024.
URL https://arxiv.org/abs/2409.03291.
Gao et al. (2024)
Leo Gao, Jonathan Tow, Baber Abbasi, Stella Biderman, Sid Black, Anthony
DiPofi, Charles Foster, Laurence Golding, Jeffrey Hsu, Alain Le Noac’h,
Haonan Li, Kyle McDonell, Niklas Muennighoff, Chris Ociepa, Jason Phang,
et al.
The language model evaluation harness, 2024.
URL https://zenodo.org/records/12608602.
Ge et al. (2024)
Yuying Ge, Sijie Zhao, Ziyun Zeng, Yixiao Ge, Chen Li, Xintao Wang, and Ying
Shan.
Making LLaMA SEE and draw with SEED tokenizer.
In International Conference on Learning Representations
(ICLR), pp. 35206–35231, 2024.
URL
https://proceedings.iclr.cc/paper_files/paper/2024/hash/97011c648eda678424f9292dadeae72e-Abstract-Conference.html.
Gehman et al. (2020)
Samuel Gehman, Suchin Gururangan, Maarten Sap, Yejin Choi, and Noah A. Smith.
RealToxicityPrompts: Evaluating neural toxic degeneration in
language models.
In Findings of the Association for Computational Linguistics:
EMNLP, pp. 3356–3369, 2020.
doi: 10.18653/v1/2020.findings-emnlp.301.
URL https://aclanthology.org/2020.findings-emnlp.301/.
Gemma Team et al. (2026)
Gemma Team, Sherif El Abd, Vaibhav Aggarwal, Robin Algayres, Alek Andreev,
Olivier Bachem, Ian Ballantyne, Cormac Brick, Victor Cărbune, Michelle
Casbon, Mayank Chaturvedi, Aditya Chawla, Victor Cotruta, Alice Coucke, Phil
Culliton, et al.
Gemma 4 technical report, 2026.
URL https://arxiv.org/abs/2607.02770.
Geng et al. (2025)
Scott Geng, Hamish Ivison, Chun-Liang Li, Maarten Sap, Jerry Li, Ranjay
Krishna, and Pang Wei Koh.
The delta learning hypothesis: Preference tuning on weak data can
yield strong gains.
In Conference on Language Modeling (COLM), 2025.
URL https://openreview.net/forum?id=9rwtezthwo.
Ghosh et al. (2025)
Shaona Ghosh, Heather Frase, Adina Williams, Sarah Luger, Paul Röttger,
Fazl Barez, Sean McGregor, Kenneth Fricklas, Mala Kumar, Quentin
Feuillade-Montixi, Kurt D. Bollacker, Felix Friedrich, Ryan Tsang, Bertie
Vidgen, Alicia Parrish, et al.
AILuminate: Introducing v1.0 of the AI risk a