Year: 2025
Type: conference-paper
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 selfcorrection, 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 is publicly available on GitHub.
Authors: Zhe Yang, Yichang Zhang, Yudong Wang, Ziyao Xu, Junyang Lin, Zhifang Sui
Institutions: Peking University, Alibaba Group (China)
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4412886705