Knowledge Editing (KE) for modifying factual knowledge in Large Language
Models (LLMs) has been receiving increasing attention. However, existing
knowledge editing methods are entity-centric, and it is unclear whether this
approach is suitable for a relation-centric perspective. To address this gap,
this paper constructs a new benchmark named RaKE, which focuses on Relation
based Knowledge Editing. In this paper, we establish a suite of innovative
metrics for evaluation and conduct comprehensive experiments involving various
knowledge editing baselines. We notice that existing knowledge editing methods
exhibit the potential difficulty in their ability to edit relations. Therefore,
we further explore the role of relations in factual triplets within the
transformer. Our research results confirm that knowledge related to relations
is not only stored in the FFN network but also in the attention layers. This
provides experimental support for future relation-based knowledge editing
methods.

该研究构建了一个名为 RaKE 的基准，专注于基于关系的知识编辑，并通过创新的评估指标和全面的实验研究，确认了关系在事实三元组中的作用，为未来的基于关系的知识编辑方法提供了实验支持。