Merging Methods for Multilingual Knowledge Editing for Large Language Models: An Empirical Odyssey

This paper investigates the effectiveness of vector merging methods for multilingual knowledge editing (MKE) in Large Language Models, focusing on reducing interference between language-specific edits. Evaluating six merging variants across two LLMs, two editing methods, and 12 languages on the MzsRE benchmark, it finds vector summation with shared covariance to be the most reliable overall strategy.