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

ACL ARR 2024 August Submission86 Authors

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 initially 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.

Paper Type: Long

Research Area: Interpretability and Analysis of Models for NLP

Research Area Keywords: Large Language Models, Self-correction, If-or-Else, Confidence

Contribution Types: Model analysis & interpretability

Languages Studied: English

Submission Number: 86