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About this paper
Zhenyu Wu, Qingkai Zeng, Zhihan Zhang, Zhaoxuan Tan, Chao Shen, Meng Jiang
A new iterative framework called Progressive Correction (PROCO) enables large language models to self-correct answers without external feedback by employing a novel "substitute verification" method that uses masked key conditions. This approach enhanced performance across arithmetic, open-domain question answering, and commonsense reasoning tasks, yielding improvements such as +7.9 EM on NQ and +9.6 accuracy on CSQA using GPT-3.5-Turbo-1106.
Key insights
- LLMs possess intrinsic self-correction capabilities when guided by a structured prompting framework like PROCO, challenging prior findings.
- The "substitute verification" method, which involves masking key conditions and having the LLM infer them, effectively enables internal error detection.
- Iterative feedback, by indicating previously identified incorrect answers, helps LLMs avoid repeating mistakes and progressively refines their responses.
Summary by alphaXiv.
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