ThinkBench: Dynamic Out-of-Distribution Evaluation for Robust LLM Reasoning

Shulin Huang; Linyi Yang; Yan Song; Shuang Chen; Leyang Cui; Ziyu Wan; Qingcheng Zeng; Ying Wen; Kun Shao; Weinan Zhang; Jun Wang; Yue Zhang

arXiv · 2502.16268

ThinkBench: Dynamic Out-of-Distribution Evaluation for Robust LLM Reasoning

Shulin Huang·Linyi Yang·Yan Song·Shuang Chen·Leyang Cui·Ziyu Wan·Qingcheng Zeng·Ying Wen·Kun Shao·Weinan Zhang·Jun Wang·Yue Zhang

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Abstract

Evaluating large language models (LLMs) poses significant challenges, particularly due to issues of data contamination and the leakage of correct answers. To address these challenges, we introduce ThinkBench, a novel evaluation framework designed to evaluate LLMs' reasoning capability robustly. ThinkBench proposes a dynamic data generation method for constructing out-of-distribution (OOD) datasets and offers an OOD dataset that contains 2,912 samples drawn from reasoning tasks. ThinkBench unifies the evaluation of reasoning models and non-reasoning models. We evaluate 16 LLMs and 4 PRMs under identical experimental conditions and show that most of the LLMs' performance are far from robust and they face a certain level of data leakage. By dynamically generating OOD datasets, ThinkBench effectively provides a reliable evaluation of LLMs and reduces the impact of data contamination.

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Shulin Huang, Linyi Yang, Yan Song, Shuang Chen, Leyang Cui, Ziyu Wan, Qingcheng Zeng, Ying Wen, Kun Shao, Weinan Zhang, Jun Wang, Yue Zhang. 2025-02-22. ThinkBench: Dynamic Out-of-Distribution Evaluation for Robust LLM Reasoning. https://arxiv.org/abs/2502.16268

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