Confidence Matters: Revisiting Intrinsic Self-Correction Capabilities of Large Language Models
CoRR(2024)
摘要
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 .
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