ThinkBench: Dynamic Out-of-Distribution Evaluation for Robust LLM Reasoning
Zhejiang University · Southern University of Science and Technology · University College London, University of London · Tencent AI Lab · Shanghai Jiao Tong University; Bytedance Seed · Northwestern University · Shanghai Jiao Tong University · Huawei Noah's Ark Lab · University College London · Westlake University
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摘要
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 robustly evaluate the reasoning capability of LLMs. 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 data contamination impact. Our data and codes are available at https://github.com/huangshulin123/ThinkBench.