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        <title>Crossing Language Boundaries: Evaluation of Large Language Models on Urdu-English Question Answering</title>
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        <namePart type="given">Samreen</namePart>
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    <abstract>This study evaluates the question-answering capabilities of Large Language Models (LLMs) in Urdu, addressing a critical gap in low-resource language processing. Four models GPT-4, mBERT, XLM-R, and mT5 are assessed across monolingual, cross-lingual, and mixed-language settings using the UQuAD1.0 and SQuAD2.0 datasets. Results reveal significant performance gaps between English and Urdu processing, with GPT-4 achieving the highest F1 scores (89.1% in English, 76.4% in Urdu) while demonstrating relative robustness in cross-lingual scenarios. Boundary detection and translation mismatches emerge as primary challenges, particularly in cross-lingual settings. The study further demonstrates that question complexity and length significantly impact performance, with factoid questions yielding 14.2% higher F1 scores compared to complex questions. These findings establish important benchmarks for enhancing LLM performance in low-resource languages and identify key areas for improvement in multilingual question-answering systems.</abstract>
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%0 Conference Proceedings
%T Crossing Language Boundaries: Evaluation of Large Language Models on Urdu-English Question Answering
%A Kazi, Samreen
%A Rahim, Maria
%A Khoja, Shakeel Ahmed
%Y Weerasinghe, Ruvan
%Y Anuradha, Isuri
%Y Sumanathilaka, Deshan
%S Proceedings of the First Workshop on Natural Language Processing for Indo-Aryan and Dravidian Languages
%D 2025
%8 January
%I Association for Computational Linguistics
%C Abu Dhabi
%F kazi-etal-2025-crossing
%X This study evaluates the question-answering capabilities of Large Language Models (LLMs) in Urdu, addressing a critical gap in low-resource language processing. Four models GPT-4, mBERT, XLM-R, and mT5 are assessed across monolingual, cross-lingual, and mixed-language settings using the UQuAD1.0 and SQuAD2.0 datasets. Results reveal significant performance gaps between English and Urdu processing, with GPT-4 achieving the highest F1 scores (89.1% in English, 76.4% in Urdu) while demonstrating relative robustness in cross-lingual scenarios. Boundary detection and translation mismatches emerge as primary challenges, particularly in cross-lingual settings. The study further demonstrates that question complexity and length significantly impact performance, with factoid questions yielding 14.2% higher F1 scores compared to complex questions. These findings establish important benchmarks for enhancing LLM performance in low-resource languages and identify key areas for improvement in multilingual question-answering systems.
%U https://aclanthology.org/2025.indonlp-1.17/
%P 141-151

Markdown (Informal)

[Crossing Language Boundaries: Evaluation of Large Language Models on Urdu-English Question Answering](https://aclanthology.org/2025.indonlp-1.17/) (Kazi et al., IndoNLP 2025)

• Crossing Language Boundaries: Evaluation of Large Language Models on Urdu-English Question Answering (Kazi et al., IndoNLP 2025)

ACL

• Samreen Kazi, Maria Rahim, and Shakeel Ahmed Khoja. 2025. Crossing Language Boundaries: Evaluation of Large Language Models on Urdu-English Question Answering. In Proceedings of the First Workshop on Natural Language Processing for Indo-Aryan and Dravidian Languages, pages 141–151, Abu Dhabi. Association for Computational Linguistics.