CRITIC: Large Language Models Can Self-Correct with Tool-Interactive
  Critiquing
Authors: name: Zhibin Gou, name: Zhihong Shao, name: Yeyun Gong, name: Yelong Shen, name: Yujiu Yang, name: Nan Duan, name: Weizhu Chen

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.