A Dynamic Multi-layer Editor for Lifelong Knowledge Adaptation

With the development of large language models, knowledge editing algorithms for updating and revising these models, particularly lifelong editing algorithms, have been continuously emerging in recent years. However, current methods typically update parameters in a single layer while overlooking the inter-layer correlations. To address this issue, we propose the DMKE algorithm. This method divides the language model into hierarchical blocks using windows and sliding strides, and selects the most suitable editing block for parameter updates by evaluating the degree of pattern matching between different blocks and the editing knowledge. Experiments demonstrate that in lifelong editing scenarios, our algorithm achieves significant performance improvements, comprehensively surpassing existing strong baseline methods.