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        <title>Continual-learning for Modelling Low-Resource Languages from Large Language Models</title>
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    <abstract>Modelling a language model for a multi-lingual scenario includes several potential challenges, among which catastrophic forgetting is the major challenge. For example, small language models (SLM) built for low-resource languages by adapting large language models (LLMs) pose the challenge of catastrophic forgetting. This work proposes to employ a continual learning strategy using parts-of-speech (POS)-based code-switching along with a replay adapter strategy to mitigate the identified gap of catastrophic forgetting while training SLM from LLM. Experiments conducted on vision language tasks such as visual question answering and language modelling task exhibits the success of the proposed architecture.</abstract>
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%0 Conference Proceedings
%T Continual-learning for Modelling Low-Resource Languages from Large Language Models
%A K, Santosh Srinath
%A Somani, Mudit
%A Padala, Varun Reddy
%A Upadhyay, Prajna
%A Das, Abhijit
%Y Demberg, Vera
%Y Inui, Kentaro
%Y Marquez, Lluís
%S Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 March
%I Association for Computational Linguistics
%C Rabat, Morocco
%@ 979-8-89176-380-7
%F k-etal-2026-continual
%X Modelling a language model for a multi-lingual scenario includes several potential challenges, among which catastrophic forgetting is the major challenge. For example, small language models (SLM) built for low-resource languages by adapting large language models (LLMs) pose the challenge of catastrophic forgetting. This work proposes to employ a continual learning strategy using parts-of-speech (POS)-based code-switching along with a replay adapter strategy to mitigate the identified gap of catastrophic forgetting while training SLM from LLM. Experiments conducted on vision language tasks such as visual question answering and language modelling task exhibits the success of the proposed architecture.
%U https://aclanthology.org/2026.eacl-long.293/
%P 6258-6275

Markdown (Informal)

[Continual-learning for Modelling Low-Resource Languages from Large Language Models](https://aclanthology.org/2026.eacl-long.293/) (K et al., EACL 2026)

• Continual-learning for Modelling Low-Resource Languages from Large Language Models (K et al., EACL 2026)

ACL

• Santosh Srinath K, Mudit Somani, Varun Reddy Padala, Prajna Upadhyay, and Abhijit Das. 2026. Continual-learning for Modelling Low-Resource Languages from Large Language Models. In Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers), pages 6258–6275, Rabat, Morocco. Association for Computational Linguistics.