Event-level Knowledge Editing
Hao Peng Xiaozhi Wang Chunyang Li Kaisheng Zeng Jiangshan Duo Yixin Cao Lei Hou Juanzi Li
Knowledge editing aims at updating knowledge of large language models (LLMs)
to prevent them from becoming outdated. Existing work edits LLMs at the level
of factual knowledge triplets. However, natural knowledge updates in the real
world come from the occurrences of new events rather than direct changes in
factual triplets. In this paper, we propose a new task setting: event-level
knowledge editing, which directly edits new events into LLMs and improves over
conventional triplet-level editing on (1) Efficiency. A single event edit leads
to updates in multiple entailed knowledge triplets. (2) Completeness. Beyond
updating factual knowledge, event-level editing also requires considering the
event influences and updating LLMs' knowledge about future trends. We construct
a high-quality event-level editing benchmark ELKEN, consisting of 1,515 event
edits, 6,449 questions about factual knowledge, and 10,150 questions about
future tendencies. We systematically evaluate the performance of various
knowledge editing methods and LLMs on this benchmark. We find that ELKEN poses
significant challenges to existing knowledge editing approaches. Our codes and
dataset are publicly released to facilitate further research.