Assessing Knowledge Editing in Language Models via Relation Perspective
Yifan Wei Xiaoyan Yu Huanhuan Ma Fangyu Lei Yixuan Weng Ran Song Kang Liu
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.