title: Internal Consistency and Self-Feedback in Large Language Models: A
    Survey
summary: Large language models (LLMs) are expected to respond accurately but often
  exhibit deficient reasoning or generate hallucinatory content. To address
  these, studies prefixed with ``Self-'' such as Self-Consistency, Self-Improve,
  and Self-Refine have been initiated. They share a commonality: involving LLMs
  evaluating and updating itself to mitigate the issues. Nonetheless, these
  efforts lack a unified perspective on summarization, as existing surveys
  predominantly focus on categorization without examining the motivations behind
  these works.
    In this paper, we summarize a theoretical framework, termed Internal
  Consistency, which offers unified explanations for phenomena such as the lack
  of reasoning and the presence of hallucinations. Internal Consistency assesses
  the coherence among LLMs' latent layer, decoding layer, and response layer
  based on sampling methodologies. Expanding upon the Internal Consistency
  framework, we introduce a streamlined yet effective theoretical framework
  capable of mining Internal Consistency, named Self-Feedback. The Self-Feedback
  framework consists of two modules: Self-Evaluation and Self-Update. This
  framework has been employed in numerous studies.
    We systematically classify these studies by tasks and lines of work;
  summarize relevant evaluation methods and benchmarks; and delve into the
  concern, ``Does Self-Feedback Really Work?'' We propose several critical
  viewpoints, including the ``Hourglass Evolution of Internal Consistency'',
  ``Consistency Is (Almost) Correctness'' hypothesis, and ``The Paradox of Latent
  and Explicit Reasoning''. Furthermore, we outline promising directions for
  future research. We have open-sourced the experimental code, reference list,
  and statistical data, available at
  https://github.com/IAAR-Shanghai/ICSFSurvey.
paper_authors._id: 669dc5402dbf53ccd21d38dc
paper_authors.name: Xun Liang
paper_authors._id: 669dc5402dbf53ccd21d38dd
paper_authors.name: Shichao Song
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paper_authors.fullname: Shichao Song
paper_authors.type: user
paper_authors.user: Ki-Seki
paper_authors._id: 669dc5402dbf53ccd21d38de
paper_authors.name: Zifan Zheng
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paper_authors.fullname: Zifan Zheng
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paper_authors.user: fan2goa1
paper_authors._id: 669dc5402dbf53ccd21d38df
paper_authors.name: Hanyu Wang
paper_authors.status: claimed_verified
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paper_authors.fullname: hanyu Wang
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paper_authors.user: UglyToilet
paper_authors._id: 669dc5402dbf53ccd21d38e0
paper_authors.name: Qingchen Yu
paper_authors.status: claimed_verified
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paper_authors.fullname: Qingchen Yu
paper_authors.type: user
paper_authors.user: Duguce
paper_authors._id: 669dc5402dbf53ccd21d38e1
paper_authors.name: Xunkai Li
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-07-22T14:46:06.667Z
paper_authors._id: 669e0047dc9c86e6ab8ff35c
paper_authors.avatarUrl: /avatars/b6d2c2a31d8cd1340277468dcd39b028.svg
paper_authors.fullname: Xunkai Li
paper_authors.type: user
paper_authors.user: DrinkIcee
paper_authors._id: 669dc5402dbf53ccd21d38e2
paper_authors.name: Rong-Hua Li
paper_authors._id: 669dc5402dbf53ccd21d38e3
paper_authors.name: Feiyu Xiong
paper_authors._id: 669dc5402dbf53ccd21d38e4
paper_authors.name: Zhiyu Li
paper_title: Internal Consistency and Self-Feedback in Large Language Models: A
    Survey
paper_summary: Large language models (LLMs) are expected to respond accurately but often
  exhibit deficient reasoning or generate hallucinatory content. To address
  these, studies prefixed with ``Self-'' such as Self-Consistency, Self-Improve,
  and Self-Refine have been initiated. They share a commonality: involving LLMs
  evaluating and updating itself to mitigate the issues. Nonetheless, these
  efforts lack a unified perspective on summarization, as existing surveys
  predominantly focus on categorization without examining the motivations behind
  these works.
    In this paper, we summarize a theoretical framework, termed Internal
  Consistency, which offers unified explanations for phenomena such as the lack
  of reasoning and the presence of hallucinations. Internal Consistency assesses
  the coherence among LLMs' latent layer, decoding layer, and response layer
  based on sampling methodologies. Expanding upon the Internal Consistency
  framework, we introduce a streamlined yet effective theoretical framework
  capable of mining Internal Consistency, named Self-Feedback. The Self-Feedback
  framework consists of two modules: Self-Evaluation and Self-Update. This
  framework has been employed in numerous studies.
    We systematically classify these studies by tasks and lines of work;
  summarize relevant evaluation methods and benchmarks; and delve into the
  concern, ``Does Self-Feedback Really Work?'' We propose several critical
  viewpoints, including the ``Hourglass Evolution of Internal Consistency'',
  ``Consistency Is (Almost) Correctness'' hypothesis, and ``The Paradox of Latent
  and Explicit Reasoning''. Furthermore, we outline promising directions for
  future research. We have open-sourced the experimental code, reference list,
  and statistical data, available at
  https://github.com/IAAR-Shanghai/ICSFSurvey.
paper_ai_summary: A theoretical framework called Internal Consistency and a Self-Feedback framework with Self-Evaluation and Self-Update modules are presented to address reasoning and hallucination in large language models by assessing coherence across model layers.
paper_ai_keywords: large language models self-consistency self-improve self-refine internal consistency latent layer decoding layer response layer self-feedback self-evaluation self-update hourglass evolution consistency hypothesis latent reasoning explicit reasoning