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to prevent them from becoming outdated.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.

知识编辑通过在大型语言模型中更新知识来防止其过时。本文提出一种新的任务设置：事件级别知识编辑，通过将新事件直接编辑到大型语言模型中来改进传统的三元组级别编辑。我们构建了一个高质量的事件级别编辑基准 ELKEN，其中包括 1,515 个事件编辑、6,449 个关于事实知识的问题和 10,150 个关于未来趋势的问题。我们系统评估了各种知识编辑方法和大型语言模型在这个基准上的性能，并发现 ELKEN 对现有知识编辑方法提出了显著挑战。我们的代码和数据集已公开发布，以促进进一步的研究。