@inproceedings{kunchukuttan-etal-2025-data,
    title = "Data and Model Centric Approaches for Expansion of Large Language Models to New languages",
    author = "Kunchukuttan, Anoop  and
      Dabre, Raj  and
      Murthy, Rudra  and
      Khan, Mohammed Safi Ur Rahman  and
      Jayakumar, Thanmay",
    editor = "Pyatkin, Valentina  and
      Vlachos, Andreas",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Tutorial Abstracts",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.emnlp-tutorials.5/",
    pages = "12--13",
    ISBN = "979-8-89176-336-4",
    abstract = "Despite the increasing pace of Large Language Model (LLM) research, a vast majority of existing LLMs mainly support English alongside a handful of high resource languages, leaving a major gap for most low-resource languages. In this tutorial, we focus on approaches to expand the language coverage of LLMs. This provides an efficient and viable path to bring LLM technologies to low-resource languages, instead of training from scratch. We look at approaches at various stages of the LLM training pipeline, like tokenizer training, pre-training, instruction tuning, alignment, evaluation, etc., where adaptations are made to support new languages. We look at data-oriented approaches as well as model-oriented approaches. We hope that our tutorial enables researchers and practitioners to work on incorporating additional languages and tasks into existing LLMs to enhance inclusivity and coverage."
}