@inproceedings{tran-utiyama-2025-enhanced,
    title = "Enhanced Zero-Shot Machine Translation via Fixed Prefix Pair Bootstrapping",
    author = "Tran, Van-Hien  and
      Utiyama, Masao",
    editor = "Ojha, Atul Kr.  and
      Liu, Chao-hong  and
      Vylomova, Ekaterina  and
      Pirinen, Flammie  and
      Washington, Jonathan  and
      Oco, Nathaniel  and
      Zhao, Xiaobing",
    booktitle = "Proceedings of the Eighth Workshop on Technologies for Machine Translation of Low-Resource Languages (LoResMT 2025)",
    month = may,
    year = "2025",
    address = "Albuquerque, New Mexico, U.S.A.",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2025.loresmt-1.2/",
    pages = "10--15",
    ISBN = "979-8-89176-230-5",
    abstract = "Zero-shot in-context learning allows large language models (LLMs) to perform tasks using only provided instructions. However, pre-trained LLMs often face calibration issues in zero-shot scenarios, leading to challenges such as hallucinations and off-target translations that compromise output quality, particularly in machine translation (MT). This paper introduces a new method to improve zero-shot MT using fixed prefix pair bootstrapping. By initializing translations with an accurate bilingual prefix pair at the start of both source and target sentences, this approach effectively guides the model to generate precise target-language outputs. Extensive evaluations across four model architectures and multiple translation directions demonstrate significant and consistent improvements, showcasing the potential of this straightforward strategy to enhance zero-shot MT performance."
}