Confidence Matters: Revisiting Intrinsic Self-Correction Capabilities of Large Language Models
<|reference_start|>Confidence Matters: Revisiting Intrinsic Self-Correction Capabilities of Large Language Models: The recent success of Large Language Models (LLMs) has catalyzed an increasing interest in their self-correction capabilities. This paper presents a comprehensive investigation into the intrinsic self-correction of LLMs, attempting to address the ongoing debate about its feasibility. Our research has identified an important latent factor - the "confidence" of LLMs - during the self-correction process. Overlooking this factor may cause the models to over-criticize themselves, resulting in unreliable conclusions regarding the efficacy of self-correction. We have experimentally observed that LLMs possess the capability to understand the "confidence" in their own responses. It motivates us to develop an "If-or-Else" (IoE) prompting framework, designed to guide LLMs in assessing their own "confidence", facilitating intrinsic self-corrections. We conduct extensive experiments and demonstrate that our IoE-based Prompt can achieve a consistent improvement regarding the accuracy of self-corrected responses over the initial answers. Our study not only sheds light on the underlying factors affecting self-correction in LLMs, but also introduces a practical framework that utilizes the IoE prompting principle to efficiently improve self-correction capabilities with "confidence". The code is available at https://github.com/MBZUAI-CLeaR/IoE-Prompting.git.<|reference_end|>
@article{li2024confidence,
    title={Confidence Matters: Revisiting Intrinsic Self-Correction Capabilities of
  Large Language Models},
    author={Loka Li, Zhenhao Chen, Guangyi Chen, Yixuan Zhang, Yusheng Su, Eric
  Xing, Kun Zhang},
    journal={arXiv preprint arXiv:2402.12563},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.12563},
    primaryClass={cs.CL cs.AI}
}