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        <title>Towards Practical and Knowledgeable LLMs for a Multilingual World: A Thesis Proposal</title>
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            <title>Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 4: Student Research Workshop)</title>
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        <name type="personal">
            <namePart type="given">Abteen</namePart>
            <namePart type="family">Ebrahimi</namePart>
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        <name type="personal">
            <namePart type="given">Samar</namePart>
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    <abstract>The frontier of large language model (LLM) development has largely been substantiated by knowledge-intensive tasks specified in English. In this proposed thesis, I argue for the key role that multilinguality occupies in the development of practical and knowledgeable LLMs.First, I consider practical methods to improve LLM’s performance on standard natural language processing (NLP) tasks by leveraging their existing multilingual knowledge.Then, I investigate the underlying multilingual knowledge of LLMs with two benchmarks: on complex reasoning, and on territorial disputes. These benchmarks reveal LLMs’ inconsistent performance across languages. I then design efficient techniques, both at inference-time and training-time, to address these discrepancies. Finally, I extend the territorial disputes benchmark to retrieval-augmented generation (RAG) setting, comparing the effects of different retrieval settings on cross-lingual robustness. My proposal shows that informed use of multilinguality enhances LLMs’ capabilities, and our understanding thereof.</abstract>
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%0 Conference Proceedings
%T Towards Practical and Knowledgeable LLMs for a Multilingual World: A Thesis Proposal
%A Li, Bryan
%Y Ebrahimi, Abteen
%Y Haider, Samar
%Y Liu, Emmy
%Y Haider, Sammar
%Y Leonor Pacheco, Maria
%Y Wein, Shira
%S Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 4: Student Research Workshop)
%D 2025
%8 April
%I Association for Computational Linguistics
%C Albuquerque, USA
%@ 979-8-89176-192-6
%F li-2025-towards
%X The frontier of large language model (LLM) development has largely been substantiated by knowledge-intensive tasks specified in English. In this proposed thesis, I argue for the key role that multilinguality occupies in the development of practical and knowledgeable LLMs.First, I consider practical methods to improve LLM’s performance on standard natural language processing (NLP) tasks by leveraging their existing multilingual knowledge.Then, I investigate the underlying multilingual knowledge of LLMs with two benchmarks: on complex reasoning, and on territorial disputes. These benchmarks reveal LLMs’ inconsistent performance across languages. I then design efficient techniques, both at inference-time and training-time, to address these discrepancies. Finally, I extend the territorial disputes benchmark to retrieval-augmented generation (RAG) setting, comparing the effects of different retrieval settings on cross-lingual robustness. My proposal shows that informed use of multilinguality enhances LLMs’ capabilities, and our understanding thereof.
%R 10.18653/v1/2025.naacl-srw.30
%U https://aclanthology.org/2025.naacl-srw.30/
%U https://doi.org/10.18653/v1/2025.naacl-srw.30
%P 301-310

Markdown (Informal)

[Towards Practical and Knowledgeable LLMs for a Multilingual World: A Thesis Proposal](https://aclanthology.org/2025.naacl-srw.30/) (Li, NAACL 2025)

• Towards Practical and Knowledgeable LLMs for a Multilingual World: A Thesis Proposal (Li, NAACL 2025)

ACL

• Bryan Li. 2025. Towards Practical and Knowledgeable LLMs for a Multilingual World: A Thesis Proposal. In Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 4: Student Research Workshop), pages 301–310, Albuquerque, USA. Association for Computational Linguistics.