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CriticBench: Benchmarking LLMs for Critique-Correct Reasoning
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The ability of Large Language Models (LLMs) to critique and refine their reasoning is crucial for their application in evaluation, feedback provision, and self-improvement. This paper introduces CriticBench, a comprehensive benchmark designed to assess LLMs' abilities to critique and rectify their reasoning across a variety of tasks. CriticBench encompasses five reasoning domains: mathematical, commonsense, symbolic, coding, and algorithmic. It compiles 15 datasets and incorporates responses from three LLM families. Utilizing CriticBench, we evaluate and dissect the performance of 17 LLMs in generation, critique, and correction reasoning, i.e., GQC reasoning. Our findings reveal: (1) a linear relationship in GQC capabilities, with critique-focused training markedly enhancing performance; (2) a task-dependent variation in correction effectiveness, with logic-oriented tasks being more amenable to correction; (3) GQC knowledge inconsistencies that decrease as model size increases; and (4) an intriguing inter-model critiquing dynamic, where stronger models are better at critiquing weaker ones, while weaker models can surprisingly surpass stronger ones in their self-critique. We hope these insights into the nuanced critique-correct reasoning of LLMs will foster further research in LLM critique and self-improvement.
Forward citations
Cited by 5 Pith papers
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RefCritic: Training Long Chain-of-Thought Critic Models with Refinement Feedback
RefCritic uses reinforcement learning with two rule-based rewards, judgment accuracy and refinement accuracy, to train long-chain-of-thought critic models that improve LLM math reasoning and detect erroneous steps.
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CriticLean: Critic-Guided Reinforcement Learning for Mathematical Formalization
A critic model trained with reinforcement learning judges semantic correctness of Lean 4 formalizations, and using it as a filter sharply improves autoformalization accuracy.
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VIS-Shepherd: Constructing Critic for LLM-based Data Visualization Generation
A 7-billion-parameter multimodal model fine-tuned on 2,500 expert critiques of data visualizations matches or beats much larger models at identifying visualization defects.
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Step-Wise Formal Verification for LLM-Based Mathematical Problem Solving
MATH-VF formalizes LLM math solutions into SimpleMath and uses a tool-augmented critic to verify each reasoning step and offer corrective feedback.
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