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    <abstract>While Knowledge Editing has been extensively studied in monolingual settings, it remains underexplored in multilingual contexts. This survey systematizes recent research on Multilingual Knowledge Editing (MKE), a growing subdomain of model editing focused on ensuring factual edits generalize reliably across languages. We present a comprehensive taxonomy of MKE methods, covering parameter-based, memory-based, fine-tuning, and hypernetwork approaches. We survey available benchmarks, summarize key findings on method effectiveness and transfer patterns, and identify persistent challenges such as cross-lingual propagation, language anisotropy, and limited evaluation for low-resource and culturally specific languages. We also discuss broader concerns such as stability and scalability of multilingual edits. Our analysis consolidates a rapidly evolving area and lays the groundwork for future progress in editable language-aware LLMs.</abstract>
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%0 Conference Proceedings
%T Editing Across Languages: A Survey of Multilingual Knowledge Editing
%A Durrani, Nadir
%A Mousi, Basel
%A Dalvi, Fahim
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-332-6
%F durrani-etal-2025-editing
%X While Knowledge Editing has been extensively studied in monolingual settings, it remains underexplored in multilingual contexts. This survey systematizes recent research on Multilingual Knowledge Editing (MKE), a growing subdomain of model editing focused on ensuring factual edits generalize reliably across languages. We present a comprehensive taxonomy of MKE methods, covering parameter-based, memory-based, fine-tuning, and hypernetwork approaches. We survey available benchmarks, summarize key findings on method effectiveness and transfer patterns, and identify persistent challenges such as cross-lingual propagation, language anisotropy, and limited evaluation for low-resource and culturally specific languages. We also discuss broader concerns such as stability and scalability of multilingual edits. Our analysis consolidates a rapidly evolving area and lays the groundwork for future progress in editable language-aware LLMs.
%R 10.18653/v1/2025.emnlp-main.803
%U https://aclanthology.org/2025.emnlp-main.803/
%U https://doi.org/10.18653/v1/2025.emnlp-main.803
%P 15906-15918

Markdown (Informal)

[Editing Across Languages: A Survey of Multilingual Knowledge Editing](https://aclanthology.org/2025.emnlp-main.803/) (Durrani et al., EMNLP 2025)

• Editing Across Languages: A Survey of Multilingual Knowledge Editing (Durrani et al., EMNLP 2025)

ACL

• Nadir Durrani, Basel Mousi, and Fahim Dalvi. 2025. Editing Across Languages: A Survey of Multilingual Knowledge Editing. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pages 15906–15918, Suzhou, China. Association for Computational Linguistics.