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        <title>Cross-lingual Editing in Multilingual Language Models</title>
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    <abstract>The training of large language models (LLMs) necessitates substantial data and computational resources, and updating outdated LLMs entails significant efforts and resources. While numerous model editing techniques (METs) have emerged to efficiently update model outputs without retraining, their effectiveness in multilingual LLMs, where knowledge is stored in diverse languages, remains an underexplored research area. This research paper introduces the cross-lingual model editing (XME) paradigm, wherein a fact is edited in one language, and the subsequent update propagation is observed across other languages. To investigate the XME paradigm, we conducted experiments using BLOOM, mBERT, and XLM-RoBERTa using the two writing scripts: Latin (English, French, and Spanish) and Indic (Hindi, Gujarati, and Bengali). The results reveal notable performance limitations of state-of-the-art METs under the XME setting, mainly when the languages involved belong to two distinct script families. These findings highlight the need for further research and development of XME techniques to address these challenges. For more comprehensive information, the dataset used in this research and the associated code are publicly available at the following [URL](https://github.com/lingo-iitgn/XME).</abstract>
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%0 Conference Proceedings
%T Cross-lingual Editing in Multilingual Language Models
%A Beniwal, Himanshu
%A D, Kowsik
%A Singh, Mayank
%Y Graham, Yvette
%Y Purver, Matthew
%S Findings of the Association for Computational Linguistics: EACL 2024
%D 2024
%8 March
%I Association for Computational Linguistics
%C St. Julian’s, Malta
%F beniwal-etal-2024-cross
%X The training of large language models (LLMs) necessitates substantial data and computational resources, and updating outdated LLMs entails significant efforts and resources. While numerous model editing techniques (METs) have emerged to efficiently update model outputs without retraining, their effectiveness in multilingual LLMs, where knowledge is stored in diverse languages, remains an underexplored research area. This research paper introduces the cross-lingual model editing (XME) paradigm, wherein a fact is edited in one language, and the subsequent update propagation is observed across other languages. To investigate the XME paradigm, we conducted experiments using BLOOM, mBERT, and XLM-RoBERTa using the two writing scripts: Latin (English, French, and Spanish) and Indic (Hindi, Gujarati, and Bengali). The results reveal notable performance limitations of state-of-the-art METs under the XME setting, mainly when the languages involved belong to two distinct script families. These findings highlight the need for further research and development of XME techniques to address these challenges. For more comprehensive information, the dataset used in this research and the associated code are publicly available at the following [URL](https://github.com/lingo-iitgn/XME).
%U https://aclanthology.org/2024.findings-eacl.140
%P 2078-2128

Markdown (Informal)

[Cross-lingual Editing in Multilingual Language Models](https://aclanthology.org/2024.findings-eacl.140) (Beniwal et al., Findings 2024)

• Cross-lingual Editing in Multilingual Language Models (Beniwal et al., Findings 2024)

ACL

• Himanshu Beniwal, Kowsik D, and Mayank Singh. 2024. Cross-lingual Editing in Multilingual Language Models. In Findings of the Association for Computational Linguistics: EACL 2024, pages 2078–2128, St. Julian’s, Malta. Association for Computational Linguistics.