A Comprehensive Study of Knowledge Editing for Large Language Models
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frequent updates to LLMs to correct outdated information or integrate new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.
6594c58ef0152a21fcaf06a0
Ningyu Zhang
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2024-03-22T08:40:04.007Z
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Ningyu Zhang
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Yunzhi Yao
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Bozhong Tian
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Peng Wang
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Shumin Deng
6594c58ef0152a21fcaf06a5
Mengru Wang
6594c58ef0152a21fcaf06a6
Zekun Xi
6594c58ef0152a21fcaf06a7
Shengyu Mao
6594c58ef0152a21fcaf06a8
Jintian Zhang
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Yuansheng Ni
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2024-01-03T12:33:53.895Z
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Yuansheng Ni
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Siyuan Cheng
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Ziwen Xu
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Xin Xu
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2024-02-15T08:29:02.381Z
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Xin Xu
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Jia-Chen Gu
claimed_verified
2024-07-23T08:15:27.376Z
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jiachen
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JiachenGu
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Yong Jiang
claimed_verified
2025-01-20T09:32:40.459Z
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Yong Jiang
user
yongjiangNLP
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Pengjun Xie
6594c58ef0152a21fcaf06b0
Fei Huang
6594c58ef0152a21fcaf06b1
Lei Liang
6594c58ef0152a21fcaf06b2
Zhiqiang Zhang
6594c58ef0152a21fcaf06b3
Xiaowei Zhu
6594c58ef0152a21fcaf06b4
Jun Zhou
6594c58ef0152a21fcaf06b5
Huajun Chen
A Comprehensive Study of Knowledge Editing for Large Language Models
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.
Knowledge editing techniques for large language models are reviewed, categorized, and benchmarked to improve efficiency and performance with specific domain modifications.
Large Language Models knowledge editing parameterization knowledge location KnowEdit benchmark external knowledge merging knowledge intrinsic knowledge