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        <title>Group-Merger: A LoRA-based Framework for Multilingual Continual Learning</title>
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        <namePart type="given">Weijian</namePart>
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            <title>Proceedings of the 1st Workshop on Multilinguality in the Era of Large Language Models (MeLLM 2026)</title>
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            <namePart type="given">Kaiyu</namePart>
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    <abstract>Multilingual continual learning (MCL) is crucial for enabling language models to adapt across diverse linguistic environments while retaining knowledge over time. Existing parameter isolation methods allocate language-specific modules but fail to leverage cross-lingual transfer, leading to inefficient parameter growth and poor generalization. Model merging based approaches suffer from severe performance degradation as the number of language-specific tasks increases, due to interference between linguistic and task-specific knowledge. To address these challenges, we propose Group-Merger, a framework that employs group-wise merging to balance parameter efficiency and continual learning performance. Our framework mitigates catastrophic forgetting across languages while enabling knowledge transfer. Extensive experiments on multilingual evaluation benchmarks demonstrate superior performance compared to existing methods.</abstract>
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%0 Conference Proceedings
%T Group-Merger: A LoRA-based Framework for Multilingual Continual Learning
%A yi, Weijian
%A Li, Hongliang
%A Xu, Jinan
%Y Huang, Kaiyu
%Y Mo, Fengran
%Y Chen, Pinzhen
%Y Jiang, Meng
%S Proceedings of the 1st Workshop on Multilinguality in the Era of Large Language Models (MeLLM 2026)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, United States
%@ 979-8-89176-430-9
%F yi-etal-2026-group
%X Multilingual continual learning (MCL) is crucial for enabling language models to adapt across diverse linguistic environments while retaining knowledge over time. Existing parameter isolation methods allocate language-specific modules but fail to leverage cross-lingual transfer, leading to inefficient parameter growth and poor generalization. Model merging based approaches suffer from severe performance degradation as the number of language-specific tasks increases, due to interference between linguistic and task-specific knowledge. To address these challenges, we propose Group-Merger, a framework that employs group-wise merging to balance parameter efficiency and continual learning performance. Our framework mitigates catastrophic forgetting across languages while enabling knowledge transfer. Extensive experiments on multilingual evaluation benchmarks demonstrate superior performance compared to existing methods.
%U https://aclanthology.org/2026.mellm-1.30/
%P 308-316

Markdown (Informal)

[Group-Merger: A LoRA-based Framework for Multilingual Continual Learning](https://aclanthology.org/2026.mellm-1.30/) (yi et al., MeLLM 2026)

• Group-Merger: A LoRA-based Framework for Multilingual Continual Learning (yi et al., MeLLM 2026)

ACL

• Weijian yi, Hongliang Li, and Jinan Xu. 2026. Group-Merger: A LoRA-based Framework for Multilingual Continual Learning. In Proceedings of the 1st Workshop on Multilinguality in the Era of Large Language Models (MeLLM 2026), pages 308–316, San Diego, United States. Association for Computational Linguistics.