CRITIC: Large Language Models Can Self-Correct with Tool-Interactive
  Critiquing
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.
646ae122086023e36edd41b2
Zhibin Gou
claimed_verified
2023-05-22T14:04:29.243Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/62dcf5d4169bd1d2ef2ca724/oRFFmJDJTLYtPRVPCweQ_.jpeg
Zhibin Gou
user
zubingou
646ae122086023e36edd41b3
Zhihong Shao
claimed_verified
2024-02-14T08:44:10.421Z
652178662d3ba46ccdf2d5d7
/avatars/bee67db7c23e967becb290f775f8be7d.svg
Zhihong Shao
user
syhia
646ae122086023e36edd41b4
Yeyun Gong
admin_assigned
2023-05-26T08:56:14.138Z
643f615aa16cd6d1f4c581de
/avatars/47753a3e82b44f81881600c52e1e8495.svg
Yeyun Gong
user
yegong
646ae122086023e36edd41b5
Yelong Shen
646ae122086023e36edd41b6
Yujiu Yang
646ae122086023e36edd41b7
Nan Duan
646ae122086023e36edd41b8
Weizhu Chen
CRITIC: Large Language Models Can Self-Correct with Tool-Interactive
  Critiquing
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, a framework that uses external tools to validate and improve LLM outputs, consistently enhances performance across various tasks.
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