AI RESEARCH
ACE: Attribution-Controlled Knowledge Editing for Multi-hop Factual Recall
arXiv CS.CL
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ArXi:2510.07896v3 Announce Type: replace 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.