@inproceedings{hb-ptaszynski-2025-rbg,
    title = "{RBG}-{AI}: Benefits of Multilingual Language Models for Low-Resource Languages",
    author = "Hb, Barathi Ganesh  and
      Ptaszynski, Michal",
    editor = "Haddow, Barry  and
      Kocmi, Tom  and
      Koehn, Philipp  and
      Monz, Christof",
    booktitle = "Proceedings of the Tenth Conference on Machine Translation",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/name-variant-enfa-fane/2025.wmt-1.100/",
    doi = "10.18653/v1/2025.wmt-1.100",
    pages = "1233--1239",
    ISBN = "979-8-89176-341-8",
    abstract = "This paper investigates how multilingual language models benefit low-resource languages through our submission to the WMT 2025 Low-Resource Indic Language Translation shared task. We explore whether languages from related families can effectively support translation for low-resource languages that were absent or underrepresented during model training. Using a quantized multilingual pretrained foundation model, we examine zero-shot translation capabilities and cross-lingual transfer effects across three language families: Tibeto-Burman, Indo-Aryan, and Austroasiatic. Our findings demonstrate that multilingual models failed to leverage linguistic similarities, particularly evidenced within the Tibeto-Burman family. The study provides insights into the practical feasibility of zero-shot translation for low-resource language settings and the role of language family relationships in multilingual model performance."
}