@inproceedings{tran-etal-2025-exploiting,
    title = "Exploiting Word Sense Disambiguation in Large Language Models for Machine Translation",
    author = "Tran, Van-Hien  and
      Dabre, Raj  and
      Kaing, Hour  and
      Song, Haiyue  and
      Tanaka, Hideki  and
      Utiyama, Masao",
    editor = "Hettiarachchi, Hansi  and
      Ranasinghe, Tharindu  and
      Rayson, Paul  and
      Mitkov, Ruslan  and
      Gaber, Mohamed  and
      Premasiri, Damith  and
      Tan, Fiona Anting  and
      Uyangodage, Lasitha",
    booktitle = "Proceedings of the First Workshop on Language Models for Low-Resource Languages",
    month = jan,
    year = "2025",
    address = "Abu Dhabi, United Arab Emirates",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2025.loreslm-1.10/",
    pages = "135--144",
    abstract = "Machine Translation (MT) has made great strides with the use of Large Language Models (LLMs) and advanced prompting techniques. However, translating sentences with ambiguous words remains challenging, especially when LLMs have limited proficiency in the source language. This paper introduces two methods to enhance MT performance by leveraging the word sense disambiguation capabilities of LLMs. The first method integrates all the available senses of an ambiguous word into the prompting template. The second method uses a pre-trained source language model to predict the correct sense of the ambiguous word, which is then incorporated into the prompting template. Additionally, we propose two prompting template styles for providing word sense information to LLMs. Experiments on the HOLLY dataset demonstrate the effectiveness of our approach in improving MT performance."
}