title: A Comprehensive Study of Knowledge Editing for Large Language Models
summary: Large Language Models (LLMs) have shown extraordinary capabilities in
  understanding and generating text that closely mirrors human communication.
  However, a primary limitation lies in the significant computational demands
  during training, arising from their extensive parameterization. This challenge
  is further intensified by the dynamic nature of the world, necessitating
  frequent updates to LLMs to correct outdated information or integrate new
  knowledge, thereby ensuring their continued relevance. Note that many
  applications demand continual model adjustments post-training to address
  deficiencies or undesirable behaviors. There is an increasing interest in
  efficient, lightweight methods for on-the-fly model modifications. To this end,
  recent years have seen a burgeoning in the techniques of knowledge editing for
  LLMs, which aim to efficiently modify LLMs' behaviors within specific domains
  while preserving overall performance across various inputs. In this paper, we
  first define the knowledge editing problem and then provide a comprehensive
  review of cutting-edge approaches. Drawing inspiration from educational and
  cognitive research theories, we propose a unified categorization criterion that
  classifies knowledge editing methods into three groups: resorting to external
  knowledge, merging knowledge into the model, and editing intrinsic knowledge.
  Furthermore, we introduce a new benchmark, KnowEdit, for a comprehensive
  empirical evaluation of representative knowledge editing approaches.
  Additionally, we provide an in-depth analysis of knowledge location, which can
  provide a deeper understanding of the knowledge structures inherent within
  LLMs. Finally, we discuss several potential applications of knowledge editing,
  outlining its broad and impactful implications.
paper_authors._id: 6594c58ef0152a21fcaf06a0
paper_authors.name: Ningyu Zhang
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paper_authors.name: Yunzhi Yao
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paper_authors.name: Bozhong Tian
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paper_authors.name: Peng Wang
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paper_authors.name: Shumin Deng
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paper_authors.name: Mengru Wang
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paper_authors.name: Zekun Xi
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paper_authors.name: Shengyu Mao
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paper_authors.name: Jintian Zhang
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paper_authors.name: Yuansheng Ni
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paper_authors.fullname: Yuansheng Ni
paper_authors.type: user
paper_authors.user: yuanshengni
paper_authors._id: 6594c58ef0152a21fcaf06aa
paper_authors.name: Siyuan Cheng
paper_authors._id: 6594c58ef0152a21fcaf06ab
paper_authors.name: Ziwen Xu
paper_authors._id: 6594c58ef0152a21fcaf06ac
paper_authors.name: Xin Xu
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paper_authors._id: 6190ab805ca89a28e9f66873
paper_authors.avatarUrl: /avatars/a677a8401360be473895494e5fb267bb.svg
paper_authors.fullname: Xin Xu
paper_authors.type: user
paper_authors.user: XinXuNLPer
paper_authors._id: 6594c58ef0152a21fcaf06ad
paper_authors.name: Jia-Chen Gu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-07-23T08:15:27.376Z
paper_authors._id: 66783dbb97ab4b3bc141c43c
paper_authors.avatarUrl: /avatars/84bfca1b98f6479d94dfdbc0780e106b.svg
paper_authors.fullname: jiachen
paper_authors.type: user
paper_authors.user: JiachenGu
paper_authors._id: 6594c58ef0152a21fcaf06ae
paper_authors.name: Yong Jiang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-01-20T09:32:40.459Z
paper_authors._id: 678ddd806aa55e76bfffb953
paper_authors.avatarUrl: /avatars/f447936c286a6a2d2874a760210b2f17.svg
paper_authors.fullname: Yong Jiang
paper_authors.type: user
paper_authors.user: yongjiangNLP
paper_authors._id: 6594c58ef0152a21fcaf06af
paper_authors.name: Pengjun Xie
paper_authors._id: 6594c58ef0152a21fcaf06b0
paper_authors.name: Fei Huang
paper_authors._id: 6594c58ef0152a21fcaf06b1
paper_authors.name: Lei Liang
paper_authors._id: 6594c58ef0152a21fcaf06b2
paper_authors.name: Zhiqiang Zhang
paper_authors._id: 6594c58ef0152a21fcaf06b3
paper_authors.name: Xiaowei Zhu
paper_authors._id: 6594c58ef0152a21fcaf06b4
paper_authors.name: Jun Zhou
paper_authors._id: 6594c58ef0152a21fcaf06b5
paper_authors.name: Huajun Chen
paper_title: A Comprehensive Study of Knowledge Editing for Large Language Models
paper_summary: Large Language Models (LLMs) have shown extraordinary capabilities in
  understanding and generating text that closely mirrors human communication.
  However, a primary limitation lies in the significant computational demands
  during training, arising from their extensive parameterization. This challenge
  is further intensified by the dynamic nature of the world, necessitating
  frequent updates to LLMs to correct outdated information or integrate new
  knowledge, thereby ensuring their continued relevance. Note that many
  applications demand continual model adjustments post-training to address
  deficiencies or undesirable behaviors. There is an increasing interest in
  efficient, lightweight methods for on-the-fly model modifications. To this end,
  recent years have seen a burgeoning in the techniques of knowledge editing for
  LLMs, which aim to efficiently modify LLMs' behaviors within specific domains
  while preserving overall performance across various inputs. In this paper, we
  first define the knowledge editing problem and then provide a comprehensive
  review of cutting-edge approaches. Drawing inspiration from educational and
  cognitive research theories, we propose a unified categorization criterion that
  classifies knowledge editing methods into three groups: resorting to external
  knowledge, merging knowledge into the model, and editing intrinsic knowledge.
  Furthermore, we introduce a new benchmark, KnowEdit, for a comprehensive
  empirical evaluation of representative knowledge editing approaches.
  Additionally, we provide an in-depth analysis of knowledge location, which can
  provide a deeper understanding of the knowledge structures inherent within
  LLMs. Finally, we discuss several potential applications of knowledge editing,
  outlining its broad and impactful implications.
paper_ai_summary: Knowledge editing techniques for large language models are reviewed, categorized, and benchmarked to improve efficiency and performance with specific domain modifications.
paper_ai_keywords: Large Language Models knowledge editing parameterization knowledge location KnowEdit benchmark external knowledge merging knowledge intrinsic knowledge