ACE: Attribution-Controlled Knowledge Editing for Multi-hop Factual
  Recall
Large Language Models (LLMs) require efficient knowledge editing (KE) to
update factual information, yet existing methods exhibit significant
performance decay in multi-hop factual recall. This failure is particularly
acute when edits involve intermediate implicit subjects within reasoning
chains. Through causal analysis, we reveal that this limitation stems from an
oversight of how chained knowledge is dynamically represented and utilized at
the neuron level. We discover that during multi hop reasoning, implicit
subjects function as query neurons, which sequentially activate corresponding
value neurons across transformer layers to accumulate information toward the
final answer, a dynamic prior KE work has overlooked. Guided by this insight,
we propose ACE: Attribution-Controlled Knowledge Editing for Multi-hop Factual
Recall, a framework that leverages neuron-level attribution to identify and
edit these critical query-value (Q-V) pathways. ACE provides a mechanistically
grounded solution for multi-hop KE, empirically outperforming state-of-the-art
methods by 9.44% on GPT-J and 37.46% on Qwen3-8B. Our analysis further reveals
more fine-grained activation patterns in Qwen3 and demonstrates that the
semantic interpretability of value neurons is orchestrated by query-driven
accumulation. These findings establish a new pathway for advancing KE
capabilities based on the principled understanding of internal reasoning
mechanisms.
68ec7ad6cd07fb414898ca3a
Jiayu Yang
68ec7ad6cd07fb414898ca3b
Yuxuan Fan
claimed_verified
2025-10-13T16:09:05.686Z
658247c592b5a9664de63882
https://cdn-avatars.huggingface.co/v1/production/uploads/658247c592b5a9664de63882/Jn03voLQjDlB3YjpSi-PI.jpeg
fan
user
EasonFan
68ec7ad6cd07fb414898ca3c
Songning Lai
68ec7ad6cd07fb414898ca3d
Shengen Wu
68ec7ad6cd07fb414898ca3e
Jiaqi Tang
68ec7ad6cd07fb414898ca3f
Chun Kang
68ec7ad6cd07fb414898ca40
Zhijiang Guo
68ec7ad6cd07fb414898ca41
Yutao Yue
ACE: Attribution-Controlled Knowledge Editing for Multi-hop Factual
  Recall
Large Language Models (LLMs) require efficient knowledge editing (KE) to
update factual information, yet existing methods exhibit significant
performance decay in multi-hop factual recall. This failure is particularly
acute when edits involve intermediate implicit subjects within reasoning
chains. Through causal analysis, we reveal that this limitation stems from an
oversight of how chained knowledge is dynamically represented and utilized at
the neuron level. We discover that during multi hop reasoning, implicit
subjects function as query neurons, which sequentially activate corresponding
value neurons across transformer layers to accumulate information toward the
final answer, a dynamic prior KE work has overlooked. Guided by this insight,
we propose ACE: Attribution-Controlled Knowledge Editing for Multi-hop Factual
Recall, a framework that leverages neuron-level attribution to identify and
edit these critical query-value (Q-V) pathways. ACE provides a mechanistically
grounded solution for multi-hop KE, empirically outperforming state-of-the-art
methods by 9.44% on GPT-J and 37.46% on Qwen3-8B. Our analysis further reveals
more fine-grained activation patterns in Qwen3 and demonstrates that the
semantic interpretability of value neurons is orchestrated by query-driven
accumulation. These findings establish a new pathway for advancing KE
capabilities based on the principled understanding of internal reasoning
mechanisms.
ACE, a framework using neuron-level attribution, enhances multi-hop factual recall in LLMs by editing critical query-value pathways, outperforming existing methods.
Large Language Models knowledge editing multi-hop factual recall causal analysis query neurons value neurons transformer layers Attribution-Controlled Knowledge Editing GPT-J Qwen3-8B semantic interpretability