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        <title>Fine-Tuning Cross-Lingual LLMs for POS Tagging in Code-Switched Contexts</title>
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            <namePart type="given">Špela</namePart>
            <namePart type="given">Arhar</namePart>
            <namePart type="family">Holdt</namePart>
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            <namePart type="given">Nikolai</namePart>
            <namePart type="family">Ilinykh</namePart>
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        <name type="personal">
            <namePart type="given">Barbara</namePart>
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            <namePart type="given">Crina</namePart>
            <namePart type="given">Madalina</namePart>
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    <abstract>Code-switching (CS) involves speakers switching between two (or potentially more) languages during conversation and is a common phenomenon in bilingual communities. The majority of NLP research has been devoted to mono-lingual language modelling. Consequentially, most models perform poorly on code-switched data. This paper investigates the effectiveness of Cross-Lingual Large Language Models on the task of POS (Part-of-Speech) tagging in code-switched contexts, once they have undergone a fine-tuning process. The models are trained on code-switched combinations of Indian languages and English. This paper also seeks to investigate whether fine-tuned models are able to generalise and POS tag code-switched combinations that were not a part of the fine-tuning dataset. Additionally, this paper presents a new metric, the S-index (Switching-Index), for measuring the level of code-switching within an utterance.</abstract>
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%0 Conference Proceedings
%T Fine-Tuning Cross-Lingual LLMs for POS Tagging in Code-Switched Contexts
%A Absar, Shayaan
%Y Holdt, Špela Arhar
%Y Ilinykh, Nikolai
%Y Scalvini, Barbara
%Y Bruton, Micaella
%Y Debess, Iben Nyholm
%Y Tudor, Crina Madalina
%S Proceedings of the Third Workshop on Resources and Representations for Under-Resourced Languages and Domains (RESOURCEFUL-2025)
%D 2025
%8 March
%I University of Tartu Library, Estonia
%C Tallinn, Estonia
%@ 978-9908-53-121-2
%F absar-2025-fine
%X Code-switching (CS) involves speakers switching between two (or potentially more) languages during conversation and is a common phenomenon in bilingual communities. The majority of NLP research has been devoted to mono-lingual language modelling. Consequentially, most models perform poorly on code-switched data. This paper investigates the effectiveness of Cross-Lingual Large Language Models on the task of POS (Part-of-Speech) tagging in code-switched contexts, once they have undergone a fine-tuning process. The models are trained on code-switched combinations of Indian languages and English. This paper also seeks to investigate whether fine-tuned models are able to generalise and POS tag code-switched combinations that were not a part of the fine-tuning dataset. Additionally, this paper presents a new metric, the S-index (Switching-Index), for measuring the level of code-switching within an utterance.
%U https://aclanthology.org/2025.resourceful-1.2/
%P 7-12

Markdown (Informal)

[Fine-Tuning Cross-Lingual LLMs for POS Tagging in Code-Switched Contexts](https://aclanthology.org/2025.resourceful-1.2/) (Absar, RESOURCEFUL 2025)

• Fine-Tuning Cross-Lingual LLMs for POS Tagging in Code-Switched Contexts (Absar, RESOURCEFUL 2025)

ACL

• Shayaan Absar. 2025. Fine-Tuning Cross-Lingual LLMs for POS Tagging in Code-Switched Contexts. In Proceedings of the Third Workshop on Resources and Representations for Under-Resourced Languages and Domains (RESOURCEFUL-2025), pages 7–12, Tallinn, Estonia. University of Tartu Library, Estonia.