@inproceedings{lin-etal-2025-large,
    title = "Can Large Language Models Translate Unseen Languages in Underrepresented Scripts?",
    author = "Lin, Dianqing  and
      Aruukhan  and
      Hou, Hongxu  and
      Sun, Shuo  and
      Chen, Wei  and
      Yang, Yichen  and
      Shi, Guo Dong",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.emnlp-main.1179/",
    pages = "23148--23161",
    ISBN = "979-8-89176-332-6",
    abstract = "Large language models (LLMs) have demonstrated impressive performance in machine translation, but still struggle with unseen low-resource languages, especially those written in underrepresented scripts. To investigate whether LLMs can translate such languages with the help of linguistic resources, we introduce Lotus, a benchmark designed to evaluate translation for Mongolian (in traditional script) and Yi. Our study shows that while linguistic resources can improve translation quality as measured by automatic metrics, LLMs remain limited in their ability to handle these languages effectively. We hope our work provides insights for the low-resource NLP community and fosters further progress in machine translation for underrepresented script low-resource languages. Our code and data are available."
}