Confidence Matters: Revisiting Intrinsic Self-Correction Capabilities of Large Language Models

• URL: http://arxiv.org/abs/2402.12563v3
• Date: Mon, 13 May 2024 11:01:17 GMT
• Title: Confidence Matters: Revisiting Intrinsic Self-Correction Capabilities of Large Language Models
• Authors: Loka Li, Zhenhao Chen, Guangyi Chen, Yixuan Zhang, Yusheng Su, Eric Xing, Kun Zhang,
• Abstract summary: Large Language Models (LLMs) have catalyzed an increasing interest in their self-correction capabilities. This paper presents a comprehensive investigation into the intrinsic self-correction of LLMs. We develop an "If-or-Else" (IoE) prompting framework, designed to guide LLMs in assessing their own "confidence"
• Score: 23.42725642076256
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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