title: CaKE: Circuit-aware Editing Enables Generalizable Knowledge Learners
summary: Knowledge Editing (KE) enables the modification of outdated or incorrect
  information in large language models (LLMs). While existing KE methods can
  update isolated facts, they struggle to generalize these updates to multi-hop
  reasoning tasks that depend on the modified knowledge. Through an analysis of
  reasoning circuits -- the neural pathways LLMs use for knowledge-based
  inference, we observe that current layer-localized KE approaches, such as MEMIT
  and WISE, which edit only single or a few model layers, struggle to effectively
  incorporate updated information into these reasoning pathways. To address this
  limitation, we propose CaKE (Circuit-aware Knowledge Editing), a novel method
  that enables more effective integration of updated knowledge in LLMs. CaKE
  leverages strategically curated data, guided by our circuits-based analysis,
  that enforces the model to utilize the modified knowledge, stimulating the
  model to develop appropriate reasoning circuits for newly integrated knowledge.
  Experimental results show that CaKE enables more accurate and consistent use of
  updated knowledge across related reasoning tasks, leading to an average of 20%
  improvement in multi-hop reasoning accuracy on MQuAKE dataset compared to
  existing KE methods. We release the code and data in
  https://github.com/zjunlp/CaKE.
paper_authors._id: 67dcd18ad2550735425351bf
paper_authors.name: Yunzhi Yao
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paper_authors.fullname: Yunzhi Yao
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paper_authors._id: 67dcd18ad2550735425351c0
paper_authors.name: Jizhan Fang
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paper_authors.fullname: Jizhan Fang
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paper_authors.user: JizhanFang
paper_authors._id: 67dcd18ad2550735425351c1
paper_authors.name: Jia-Chen Gu
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paper_authors.fullname: Jia-Chen Gu
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paper_authors.user: gujc
paper_authors._id: 67dcd18ad2550735425351c2
paper_authors.name: Ningyu Zhang
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paper_authors.fullname: Ningyu Zhang
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paper_authors.user: Ningyu
paper_authors._id: 67dcd18ad2550735425351c3
paper_authors.name: Shumin Deng
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paper_authors.fullname: Shumin Deng
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paper_authors.user: 231sm
paper_authors._id: 67dcd18ad2550735425351c4
paper_authors.name: Huajun Chen
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-21T13:07:15.045Z
paper_authors._id: 67c06592ebc2e682b2628bde
paper_authors.avatarUrl: /avatars/1032c0319eded956d2dd461b0e1b4ddb.svg
paper_authors.fullname: Huajun Chen
paper_authors.type: user
paper_authors.user: HuajunChen
paper_authors._id: 67dcd18ad2550735425351c5
paper_authors.name: Nanyun Peng
paper_title: CaKE: Circuit-aware Editing Enables Generalizable Knowledge Learners
paper_summary: Knowledge Editing (KE) enables the modification of outdated or incorrect
  information in large language models (LLMs). While existing KE methods can
  update isolated facts, they struggle to generalize these updates to multi-hop
  reasoning tasks that depend on the modified knowledge. Through an analysis of
  reasoning circuits -- the neural pathways LLMs use for knowledge-based
  inference, we observe that current layer-localized KE approaches, such as MEMIT
  and WISE, which edit only single or a few model layers, struggle to effectively
  incorporate updated information into these reasoning pathways. To address this
  limitation, we propose CaKE (Circuit-aware Knowledge Editing), a novel method
  that enables more effective integration of updated knowledge in LLMs. CaKE
  leverages strategically curated data, guided by our circuits-based analysis,
  that enforces the model to utilize the modified knowledge, stimulating the
  model to develop appropriate reasoning circuits for newly integrated knowledge.
  Experimental results show that CaKE enables more accurate and consistent use of
  updated knowledge across related reasoning tasks, leading to an average of 20%
  improvement in multi-hop reasoning accuracy on MQuAKE dataset compared to
  existing KE methods. We release the code and data in
  https://github.com/zjunlp/CaKE.
paper_ai_summary: CaKE enhances large language models' multi-hop reasoning by targeting specific neural pathways for knowledge editing, improving accuracy compared to existing methods.
paper_ai_keywords: knowledge editing large language models multi-hop reasoning reasoning circuits MEMIT WISE circuit-aware knowledge editing knowledge-based inference