EVEDIT: Event-based Knowledge Editing with Deductive Editing Boundaries
Jiateng Liu Pengfei Yu Yuji Zhang Sha Li Zixuan Zhang Heng Ji
The dynamic nature of real-world information necessitates efficient knowledge
editing (KE) in large language models (LLMs) for knowledge updating. However,
current KE approaches, which typically operate on (subject, relation, object)
triples, ignore the contextual information and the relation among different
knowledge. Such editing methods could thus encounter an uncertain editing
boundary, leaving a lot of relevant knowledge in ambiguity: Queries that could
be answered pre-edit cannot be reliably answered afterward. In this work, we
analyze this issue by introducing a theoretical framework for KE that
highlights an overlooked set of knowledge that remains unchanged and aids in
knowledge deduction during editing, which we name as the deduction anchor. We
further address this issue by proposing a novel task of event-based knowledge
editing that pairs facts with event descriptions. This task manifests not only
a closer simulation of real-world editing scenarios but also a more logically
sound setting, implicitly defining the deduction anchor to address the issue of
indeterminate editing boundaries. We empirically demonstrate the superiority of
event-based editing over the existing setting on resolving uncertainty in
edited models, and curate a new benchmark dataset EvEdit derived from the
CounterFact dataset. Moreover, while we observe that the event-based setting is
significantly challenging for existing approaches, we propose a novel approach
Self-Edit that showcases stronger performance, achieving 55.6% consistency
improvement while maintaining the naturalness of generation.