Knowledge editing aims to rectify inaccuracies in large language models (LLMs) without costly retraining for outdated or erroneous knowledge.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. In this paper, lifelong editing is synonymous with lifelong knowledge 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 WilKE, which selects editing layer based on the pattern matching degree of editing knowledge across different layers. 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.

知识编辑致力于在大型语言模型（LLMs）中纠正过时或错误的知识，无需昂贵的重新训练。然而，当前的知识编辑方法主要集中在单一编辑上，无法满足终身编辑的要求。本文揭示了终身编辑中知识编辑遇到的性能退化问题，其特征是毒性堆积和毒性闪现，主要原因是模式不匹配。我们引入了一种名为WilKE的知识编辑方法，它根据跨不同层的编辑知识的模式匹配度来选择编辑层。实验结果表明，在终身编辑中，WilKE相对于最先进的知识编辑方法，在编辑GPT2-XL和GPT-J上平均改进了46.2％和67.8％。

WilKE：智能层知识编辑器用于终身知识编辑