Large Language Models Can Self-correct With Minimal Effort
Wu Zhenyu, Zeng Qingkai, Zhang Zhihan, Tan Zhaoxuan, Shen Chao, Jiang Meng. Arxiv 2024
[Paper]
Intrinsic self-correct was a method that instructed large language models (LLMs) to verify and correct their responses without external feedback. Unfortunately, the study concluded that the LLMs could not self-correct reasoning yet. We find that a simple yet effective verification method can unleash inherent capabilities of the LLMs. That is to mask a key condition in the question, add the current response to construct a verification question, and predict the condition to verify the response. The condition can be an entity in an open-domain question or a numeric value in a math question, which requires minimal effort (via prompting) to identify. We propose an iterative verify-then-correct framework to progressively identify and correct (probably) false responses, named ProCo. We conduct experiments on three reasoning tasks. On average, ProCo, with GPT-3.5-Turbo as the backend LLM, yields \(+6.8\) exact match on four open-domain question answering datasets, \(+14.1\) accuracy on three arithmetic reasoning datasets, and \(+9.6\) accuracy on a commonsense reasoning dataset, compared to Self-Correct.
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