ensure deterministic outputs. These settings allow our reported results to be reliably reproduced.
Guidelines:
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The answer NA means that paper does not include experiments requiring code.
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Please see the NeurIPS code and data submission guidelines (https://nips.cc/public/guides/CodeSubmissionPolicy) for more details.
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At submission time, to preserve anonymity, the authors should release anonymized versions (if applicable).
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Providing as much information as possible in supplemental material (appended to the paper) is recommended, but including URLs to data and code is permitted.
6.
Experimental setting/details
Question: Does the paper specify all the training and test details (e.g., data splits, hyperparameters, how they were chosen, type of optimizer, etc.) necessary to understand the results?
Answer: [Yes]
Justification: In Section 4.
Guidelines:
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The answer NA means that the paper does not include experiments.
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The experimental setting should be presented in the core of the paper to a level of detail that is necessary to appreciate the results and make sense of them.
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The full details can be provided either with the code, in appendix, or as supplemental material.
7.
Experiment statistical significance
Question: Does the paper report error bars suitably and correctly defined or other appropriate information about the statistical significance of the experiments?
Answer: [N/A]
Justification: To minimize randomness and ensure reproducibility, all models were evaluated using fixed decoding settings. Specifically, we set temperature to 0 for proprietary models and do_sample to False for open-source models. As a result, model outputs are consistent across runs, and standard statistical significance tests or error bars are not applicable in this setting.
Guidelines:
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The answer NA means that the paper does not include experiments.
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The authors should answer "Yes" if the results are accompanied by error bars, confidence intervals, or statistical significance tests, at least for the experiments that support the main claims of the paper.
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The factors of variability that the error bars are capturing should be clearly stated (for example, train/test split, initialization, random drawing of some parameter, or overall run with given experimental conditions).
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The method for calculating the error bars should be explained (closed form formula, call to a library function, bootstrap, etc.)
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The assumptions made should be given (e.g., Normally distributed errors).
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It should be clear whether the error bar is the standard deviation or the standard error of the mean.
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It is OK to report 1-sigma error bars, but one should state it. The authors should preferably report a 2-sigma error bar than state that they have a 96% CI, if the hypothesis of Normality of errors is not verified.
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For asymmetric distributions, the authors should be careful not to show in tables or figures symmetric error bars that would yield results that are out of range (e.g. negative error rates).
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If error bars are reported in tables or plots, The authors should expl