Confidence v.s. Critique: A Decomposition of Self-Correction Capability for LLMs

• URL: http://arxiv.org/abs/2412.19513v1
• Date: Fri, 27 Dec 2024 08:09:11 GMT
• Title: Confidence v.s. Critique: A Decomposition of Self-Correction Capability for LLMs
• Authors: Zhe Yang, Yichang Zhang, Yudong Wang, Ziyao Xu, Junyang Lin, Zhifang Sui,
• Abstract summary: Large Language Models (LLMs) can correct their self-generated responses, but a decline in accuracy after self-correction is also witnessed. We decompose the self-correction capability into confidence (being confident to correct answers) and critique (turning wrong answers to correct) capabilities. Our strategy outperforms vanilla SFT in both capabilities and achieves much higher accuracy after self-correction.
• Score: 34.203575667558454
• License:
• Abstract: Large Language Models (LLMs) can correct their self-generated responses, but a decline in accuracy after self-correction is also witnessed. To have a deeper understanding of self-correction, we endeavor to decompose, evaluate, and analyze the self-correction behaviors of LLMs. By enumerating and analyzing answer correctness before and after self-correction, we decompose the self-correction capability into confidence (being confident to correct answers) and critique (turning wrong answers to correct) capabilities, and propose two metrics from a probabilistic perspective to measure these 2 capabilities, along with another metric for overall self-correction capability evaluation. Based on our decomposition and evaluation metrics, we conduct extensive experiments and draw some empirical conclusions. For example, we find different models can exhibit distinct behaviors: some models are confident while others are more critical. We also find the trade-off between the two capabilities (i.e. improving one can lead to a decline in the other) when manipulating model self-correction behavior by prompts or in-context learning. Further, we find a simple yet efficient strategy to improve self-correction capability by transforming Supervision Fine-Tuning (SFT) data format, and our strategy outperforms vanilla SFT in both capabilities and achieves much higher accuracy after self-correction. Our code will be publicly available on GitHub.

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