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

Multilingual knowledge editing (MKE) remains challenging because language-specific edits interfere with one another, even when locate-then-edit methods succeed in monolingual settings. We study whether vector merging—combining independently computed per-language updates into a single edit—can mitigate this interference. We evaluate six merging variants with two backbone large language models, two base knowledge editing methods, and 12 languages on the MzsRE benchmark under a large-scale batch-editing setting, and we examine how the weight scaling factor and the rank compression ratio affect editing performance. Summation with shared covariance proves the most reliable strategy overall, whereas naive summation without shared covariance performs poorly. Task Singular Vectors for Merging (TSVM) helps only in specific settings, so its ability to reduce multilingual interference is limited. Performance is also sensitive to both weight scale and rank ratio, with larger-than-default scaling and relatively low rank often yielding the best results. When the results are analyzed by language-resource level, the choice of merging method matters most for the relatively low-resource languages, such as Thai and Vietnamese. These findings clarify the practical strengths and limits of current vector merging methods for MKE and provide guidance for future multilingual knowledge editing research.