CRITIC：大型语言模型可以通过工具交互式批判进行自我纠正。

摘要

最近大型语言模型（LLMs）的发展令人印象深刻。然而，这些模型有时会显示不一致和有问题的行为，比如产生虚构事实、生成有缺陷的代码，或者创造冒犯性和有毒内容。与这些模型不同，人类通常利用外部工具来交叉检查和完善他们的初始内容，比如使用搜索引擎进行事实核查，或者使用代码解释器进行调试。受到这一观察的启发，我们引入了一个名为CRITIC的框架，允许LLMs这种本质上的“黑匣子”验证和逐渐修正其自身的输出，类似于人类与工具的交互。更具体地说，从初始输出开始，CRITIC与适当的工具进行交互，评估文本的某些方面，然后根据在此验证过程中获得的反馈修订输出。涉及自由形式问答、数学程序合成和毒性减少的全面评估表明，CRITIC始终提升了LLMs的性能。与此同时，我们的研究突显了外部反馈在促进LLMs持续自我改进中的关键重要性。

English

Recent developments in large language models (LLMs) have been impressive. However, these models sometimes show inconsistencies and problematic behavior, such as hallucinating facts, generating flawed code, or creating offensive and toxic content. Unlike these models, humans typically utilize external tools to cross-check and refine their initial content, like using a search engine for fact-checking, or a code interpreter for debugging. Inspired by this observation, we introduce a framework called CRITIC that allows LLMs, which are essentially "black boxes" to validate and progressively amend their own outputs in a manner similar to human interaction with tools. More specifically, starting with an initial output, CRITIC interacts with appropriate tools to evaluate certain aspects of the text, and then revises the output based on the feedback obtained during this validation process. Comprehensive evaluations involving free-form question answering, mathematical program synthesis, and toxicity reduction demonstrate that CRITIC consistently enhances the performance of LLMs. Meanwhile, our research highlights the crucial importance of external feedback in promoting the ongoing self-improvement of LLMs.

CRITIC：大型语言模型可以通过工具交互式批判进行自我纠正。

CRITIC: Large Language Models Can Self-Correct with Tool-Interactive Critiquing

摘要

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