Summary of Criticbench: Benchmarking Llms For Critique-correct Reasoning, by Zicheng Lin et al.
CriticBench: Benchmarking LLMs for Critique-Correct Reasoning
by Zicheng Lin, Zhibin Gou, Tian Liang, Ruilin Luo, Haowei Liu, Yujiu Yang
First submitted to arxiv on: 22 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Medium Difficulty summary: This paper presents CriticBench, a benchmark designed to evaluate Large Language Models’ (LLMs) abilities to critique and rectify their reasoning across various tasks. CriticBench includes five reasoning domains and 15 datasets, assessing the performance of 17 LLMs in generation, critique, and correction reasoning. The findings reveal a linear relationship between GQC capabilities, with critique-focused training improving performance; task-dependent variations in correction effectiveness; inconsistencies in GQC knowledge decreasing as model size increases; and an intriguing inter-model critiquing dynamic. These insights aim to foster further research in LLM critique and self-improvement. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: This paper creates a test for large language models (LLMs) to see how well they can think critically and fix their mistakes. The test, called CriticBench, has five areas where the LLMs need to reason correctly. It uses 15 different datasets to see how well the LLMs perform in tasks like math problems, common sense questions, coding challenges, and algorithm puzzles. The results show that some LLMs are better at thinking critically than others, but it’s not just about being big or small – some smaller models can actually be better at fixing their mistakes than bigger ones. This research helps us understand how to make language models better at learning from their mistakes. |
