A new eight-task benchmark with in-domain metrics KGI and KPI reveals that existing multimodal editing methods degrade on related samples, and the proposed HICE method achieves a better balance.
WilKE: Wise-Layer Knowledge Editor for Lifelong Knowledge Editing
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
abstract
Knowledge editing aims to rectify inaccuracies in large language models (LLMs) without costly retraining for outdated or erroneous knowledge. However, current knowledge editing methods primarily focus on single editing, failing to meet the requirements for lifelong editing. This study reveals a performance degradation encountered by knowledge editing in lifelong editing, characterized by toxicity buildup and toxicity flash, with the primary cause identified as pattern unmatch. We introduce a knowledge editing approach named Wise-Layer Knowledge Editor (WilKE), which selects editing layer based on the pattern matching degree of editing knowledge across different layers in language models. Experimental results demonstrate that, in lifelong editing, WilKE exhibits an average improvement of 46.2% and 67.8% on editing GPT2-XL and GPT-J relative to state-of-the-art knowledge editing methods.
fields
cs.CV 1years
2024 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
ComprehendEdit: A Comprehensive Dataset and Evaluation Framework for Multimodal Knowledge Editing
A new eight-task benchmark with in-domain metrics KGI and KPI reveals that existing multimodal editing methods degrade on related samples, and the proposed HICE method achieves a better balance.