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.

大型语言模型 (LLMs) 的内在自我纠正能力受到了研究，然而，通过掩盖问题中的关键条件，添加当前答案构建验证问题，并预测条件以验证答案的简单但有效的验证方法，提出了逐步识别和纠正可能的错误响应的迭代验证 - 纠正框架，命名为 ProCo。与自我纠正相比，ProCo 在三种推理任务中的性能明显提高。