Knowledge Editing (KE) enables the modification of outdated or incorrect information in large language models (LLMs).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 often fail to generalize these updates to multi-hop reasoning tasks that rely on the modified knowledge. Through an analysis of reasoning circuits -- the neural pathways LLMs use for knowledge-based inference, we find that current layer-localized KE approaches (e.g., MEMIT, WISE), which edit only single or a few model layers, inadequately integrate updated knowledge into these reasoning pathways. To address this limitation, we present CaKE (Circuit-aware Knowledge Editing), a novel method that enhances the effective integration of updated knowledge in LLMs. By only leveraging a few curated data samples guided by our circuit-based analysis, CaKE stimulates the model to develop appropriate reasoning circuits for newly incorporated knowledge. Experiments show that CaKE enables more accurate and consistent use of edited knowledge across related reasoning tasks, achieving an average improvement of 20% in multi-hop reasoning accuracy on the MQuAKE dataset while requiring less memory than existing KE methods. We release the code and data in https://github.com/zjunlp/CaKE.

翻译：知识编辑（Knowledge Editing, KE）能够修正大型语言模型（LLMs）中过时或错误的信息。现有KE方法虽可更新孤立事实，却常无法将更新泛化至依赖修改知识的多跳推理任务。通过对推理电路（即LLMs用于知识推断的神经通路）的分析，我们发现当前基于局部层编辑的KE方法（如MEMIT、WISE）仅修改单个或少数模型层，难以将更新知识有效整合至推理通路。为此，我们提出CaKE（Circuit-aware Knowledge Editing），一种增强LLMs中更新知识有效整合的新方法。该方法仅需借助基于电路分析筛选的少量样本数据，即可激发模型为新增知识构建适配的推理电路。实验表明，CaKE能在相关推理任务中更准确、一致地运用编辑后知识，在MQuAKE数据集上实现多跳推理准确率平均提升20%，同时所需内存低于现有KE方法。代码与数据已发布于https://github.com/zjunlp/CaKE。