Avoiding Knowledge Edit Skipping in Multi-hop Question Answering with Guided Decomposition

• URL: http://arxiv.org/abs/2509.07555v1
• Date: Tue, 09 Sep 2025 09:49:23 GMT
• Title: Avoiding Knowledge Edit Skipping in Multi-hop Question Answering with Guided Decomposition
• Authors: Yi Liu, Xiangrong Zhu, Xiangyu Liu, Wei Wei, Wei Hu,
• Abstract summary: We find that existing retrieval-augmented generation (RAG)-based knowledge editing methods struggle with multi-hop question answering.<n>We propose a novel Iterative Retrieval-Augmented Knowledge Editing method with guided decomposition.<n> Experimental results demonstrate that IRAKE mitigates the failure of editing caused by edit skipping and outperforms state-of-the-art methods for KE in multi-hop question answering.
• Score: 32.73672881869734
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In a rapidly evolving world where information updates swiftly, knowledge in large language models (LLMs) becomes outdated quickly. Retraining LLMs is not a cost-effective option, making knowledge editing (KE) without modifying parameters particularly necessary. We find that although existing retrieval-augmented generation (RAG)-based KE methods excel at editing simple knowledge, they struggle with KE in multi-hop question answering due to the issue of "edit skipping", which refers to skipping the relevant edited fact in inference. In addition to the diversity of natural language expressions of knowledge, edit skipping also arises from the mismatch between the granularity of LLMs in problem-solving and the facts in the edited memory. To address this issue, we propose a novel Iterative Retrieval-Augmented Knowledge Editing method with guided decomposition (IRAKE) through the guidance from single edited facts and entire edited cases. Experimental results demonstrate that IRAKE mitigates the failure of editing caused by edit skipping and outperforms state-of-the-art methods for KE in multi-hop question answering.

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