title: Large Language Models Cannot Self-Correct Reasoning Yet
summary: Large Language Models (LLMs) have emerged as a groundbreaking technology with
  their unparalleled text generation capabilities across various applications.
  Nevertheless, concerns persist regarding the accuracy and appropriateness of
  their generated content. A contemporary methodology, self-correction, has been
  proposed as a remedy to these issues. Building upon this premise, this paper
  critically examines the role and efficacy of self-correction within LLMs,
  shedding light on its true potential and limitations. Central to our
  investigation is the notion of intrinsic self-correction, whereby an LLM
  attempts to correct its initial responses based solely on its inherent
  capabilities, without the crutch of external feedback. In the context of
  reasoning, our research indicates that LLMs struggle to self-correct their
  responses without external feedback, and at times, their performance might even
  degrade post self-correction. Drawing from these insights, we offer suggestions
  for future research and practical applications in this field.
paper_authors._id: 651cdd0628950a077034c66a
paper_authors.name: Jie Huang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-10-16T07:25:03.502Z
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paper_authors.avatarUrl: /avatars/b5bc04180353fb9ef1474f01185050d3.svg
paper_authors.fullname: Jie Huang
paper_authors.type: user
paper_authors.user: jeffhj
paper_authors._id: 651cdd0628950a077034c66b
paper_authors.name: Xinyun Chen
paper_authors._id: 651cdd0628950a077034c66c
paper_authors.name: Swaroop Mishra
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-10-04T12:39:09.554Z
paper_authors._id: 61841feb7c380c66d40cad54
paper_authors.avatarUrl: /avatars/7a79362a24b844860781fb0625527eae.svg
paper_authors.fullname: Swaroop Mishra
paper_authors.type: user
paper_authors.user: swaroop7
paper_authors._id: 651cdd0628950a077034c66d
paper_authors.name: Huaixiu Steven Zheng
paper_authors._id: 651cdd0628950a077034c66e
paper_authors.name: Adams Wei Yu
paper_authors._id: 651cdd0628950a077034c66f
paper_authors.name: Xinying Song
paper_authors._id: 651cdd0628950a077034c670
paper_authors.name: Denny Zhou
paper_authors.status: extracted_confirmed
paper_authors.statusLastChangedAt: 2024-02-20T16:43:56.732Z
paper_authors._id: 65d4d62d493aeaf0bb295f1b
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/vXMxaoWwFziul8TqIhQw8.jpeg
paper_authors.fullname: Denny Zhou
paper_authors.type: user
paper_authors.user: dennyzhou
paper_title: Large Language Models Cannot Self-Correct Reasoning Yet
paper_summary: Large Language Models (LLMs) have emerged as a groundbreaking technology with
  their unparalleled text generation capabilities across various applications.
  Nevertheless, concerns persist regarding the accuracy and appropriateness of
  their generated content. A contemporary methodology, self-correction, has been
  proposed as a remedy to these issues. Building upon this premise, this paper
  critically examines the role and efficacy of self-correction within LLMs,
  shedding light on its true potential and limitations. Central to our
  investigation is the notion of intrinsic self-correction, whereby an LLM
  attempts to correct its initial responses based solely on its inherent
  capabilities, without the crutch of external feedback. In the context of
  reasoning, our research indicates that LLMs struggle to self-correct their
  responses without external feedback, and at times, their performance might even
  degrade post self-correction. Drawing from these insights, we offer suggestions
  for future research and practical applications in this field.
paper_ai_summary: This paper evaluates the intrinsic self-correction capabilities of Large Language Models and identifies limitations in their ability to improve responses without external feedback.
paper_ai_keywords: Large Language Models LLMs self-correction intrinsic self-correction