CaKE：电路感知编辑实现通用知识学习
CaKE: Circuit-aware Editing Enables Generalizable Knowledge Learners
AI总结：
CaKE通过电路感知编辑提升LLMs对更新知识的多跳推理能力，实现20%的准确率提升并降低内存消耗
摘要：
知识编辑（KE）使大型语言模型（LLMs）能够修改过时或错误的信息。尽管现有的KE方法可以更新孤立的事实，但它们往往无法将这些更新泛化到依赖修改知识的多跳推理任务。通过分析推理电路——LLMs用于基于知识推理的神经路径，我们发现当前层局部化的KE方法（如MEMIT、WISE），只编辑单个或几个模型层，无法充分将更新的知识整合到这些推理路径中。为了解决这一限制，我们提出了CaKE（电路感知知识编辑），一种新的方法，增强了LLMs中更新知识的有效整合。通过仅利用少量经过精心挑选的数据样本，这些样本由我们的基于电路的分析引导，CaKE促使模型发展出适合新纳入知识的适当推理电路。实验表明，CaKE使模型在相关推理任务中更准确且一致地使用编辑后的知识，在MQuAKE数据集上实现多跳推理准确率平均提高20%，且比现有KE方法占用更少的内存。我们已在https://github.com/zjunlp/CaKE发布代码和数据。
英文摘要：
Knowledge Editing (KE) enables the modification of outdated or incorrect information in large language models (LLMs).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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
