title: CRITIC: Large Language Models Can Self-Correct with Tool-Interactive
    Critiquing
summary: 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.
paper_authors._id: 646ae122086023e36edd41b2
paper_authors.name: Zhibin Gou
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-05-22T14:04:29.243Z
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paper_authors.fullname: Zhibin Gou
paper_authors.type: user
paper_authors.user: zubingou
paper_authors._id: 646ae122086023e36edd41b3
paper_authors.name: Zhihong Shao
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-02-14T08:44:10.421Z
paper_authors._id: 652178662d3ba46ccdf2d5d7
paper_authors.avatarUrl: /avatars/bee67db7c23e967becb290f775f8be7d.svg
paper_authors.fullname: Zhihong Shao
paper_authors.type: user
paper_authors.user: syhia
paper_authors._id: 646ae122086023e36edd41b4
paper_authors.name: Yeyun Gong
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-05-26T08:56:14.138Z
paper_authors._id: 643f615aa16cd6d1f4c581de
paper_authors.avatarUrl: /avatars/47753a3e82b44f81881600c52e1e8495.svg
paper_authors.fullname: Yeyun Gong
paper_authors.type: user
paper_authors.user: yegong
paper_authors._id: 646ae122086023e36edd41b5
paper_authors.name: Yelong Shen
paper_authors._id: 646ae122086023e36edd41b6
paper_authors.name: Yujiu Yang
paper_authors._id: 646ae122086023e36edd41b7
paper_authors.name: Nan Duan
paper_authors._id: 646ae122086023e36edd41b8
paper_authors.name: Weizhu Chen
paper_title: CRITIC: Large Language Models Can Self-Correct with Tool-Interactive
    Critiquing
paper_summary: 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.
paper_ai_summary: CRITIC, a framework that uses external tools to validate and improve LLM outputs, consistently enhances performance across various tasks.
paper_ai_keywords: large language models LLMs hallucinating facts generating flawed code offensive and toxic content CRITIC fact-checking code interpreter black boxes validation output revision free-form question answering mathematical program synthesis toxicity reduction