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

• URL: http://arxiv.org/abs/2502.16268v1
• Date: Sat, 22 Feb 2025 15:41:51 GMT
• Title: ThinkBench: Dynamic Out-of-Distribution Evaluation for Robust LLM Reasoning
• Authors: Shulin Huang, Linyi Yang, Yan Song, Shuang Chen, Leyang Cui, Ziyu Wan, Qingcheng Zeng, Ying Wen, Kun Shao, Weinan Zhang, Jun Wang, Yue Zhang,
• Abstract summary: ThinkBench is an evaluation framework for large language models (LLMs)<n>It unifies the evaluation of reasoning models and non-reasoning models.<n>ThinkBench effectively provides a reliable evaluation of LLMs and reduces the impact of data contamination.
• Score: 61.750373974799366
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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