Serial Lifelong Editing via Mixture of Knowledge Experts

摘要

It is challenging to update Large language models (LLMs) since real-world knowledge evolves. While existing Lifelong Knowledge Editing (LKE) methods efficiently update sequentially incoming edits, they often struggle to precisely overwrite the outdated knowledge with the latest one, resulting in conflicts that hinder LLMs from determining the correct answer. To address this \textbf{S}erial \textbf{L}ifelong \textbf{K}nowledge \textbf{E}diting (sLKE) problem, wepropose a novel Mixture-of-Knowledge-Experts scheme with an \textbf{A}ctivation-guided \textbf{R}outing \textbf{M}echanism (ARM), which assigns specialized experts to store domain-specific knowledge and ensures that each update completely overwrites old information with the latest data. Furthermore, we introduce a novel sLKE benchmark where answers to the same concept are updated repeatedly, to assess the ability of editing methods to refresh knowledge accurately. Experimental results on both LKE and sLKE benchmarks show that our ARM performs favorably against SOTA knowledge editing methods.