@inproceedings{li-2025-towards,
    title = "Towards Practical and Knowledgeable {LLM}s for a Multilingual World: A Thesis Proposal",
    author = "Li, Bryan",
    editor = "Ebrahimi, Abteen  and
      Haider, Samar  and
      Liu, Emmy  and
      Haider, Sammar  and
      Leonor Pacheco, Maria  and
      Wein, Shira",
    booktitle = "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)",
    month = apr,
    year = "2025",
    address = "Albuquerque, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2025.naacl-srw.30/",
    pages = "301--310",
    ISBN = "979-8-89176-192-6",
    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 \textit{practical} and \textit{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."
}