Merging Methods for Multilingual Knowledge Editing for Large Language Models: An Empirical Odyssey
This paper systematically evaluates six vector merging methods for multilingual knowledge editing (MKE). Vector summation with shared covariance is the most reliable strategy, while simple summation performs poorly. TSVM offers limited improvement in reducing multilingual interference. Performance is sensitive to weight scaling and rank compression ratio, with larger scaling and lower rank often yielding better results.
[2605.13919] Merging Methods for Multilingual Knowledge Editing for Large Language Models: An Empirical Odyssey
[Submitted on 13 May 2026]
Title:Merging Methods for Multilingual Knowledge Editing for Large Language Models: An Empirical Odyssey
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Abstract: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. We evaluate six merging variants with two popular backbone large language models, two base knowledge editing methods, and 12 languages on the MzsRE benchmark under a large-scale batch-editing setting. Our results show that vector summation with shared covariance is the most reliable overall strategy, whereas simple summation without shared covariance performs poorly. TSVM improves performance in some settings, but its ability to mitigate multilingual interference is limited. We also find that performance is sensitive to both weight scale and rank ratio, with larger-than-default scaling and relatively low rank often yielding better results. These findings clarify the practical strengths and limits of current vector merging methods for MKE and provide guidance for future multilingual knowledge editing research.
Subjects:
Computation and Language (cs.CL); Machine Learning (cs.LG)
Cite as: arXiv:2605.13919 [cs.CL]
(or arXiv:2605.13919v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2605.13919
arXiv-issued DOI via DataCite
Submission history
From: Kunil Lee [view email] [v1] Wed, 13 May 2026 13:22:45 UTC (987 KB)
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