AI RESEARCH
Merging Methods for Multilingual Knowledge Editing for Large Language Models: An Empirical Odyssey
arXiv CS.LG
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ArXi:2605.13919v1 Announce Type: cross Multilingual knowledge editing (MKE) remains challenging because language-specific edits interfere with one another, even when locate-then-edit methods work well in monolingual settings. This paper focuses on three issues: the effectiveness of vector merging methods for MKE, the extent to which Task Singular Vectors for Merging (TSVM) can reduce multilingual interference, and the influence of the weight scaling factor and rank compression ratio on performance.