@inproceedings{khadka-bhattarai-2025-gender,
    title = "Gender Bias in {N}epali-{E}nglish Machine Translation: A Comparison of {LLM}s and Existing {MT} Systems",
    author = "Khadka, Supriya  and
      Bhattarai, Bijayan",
    editor = "Fale{\'n}ska, Agnieszka  and
      Basta, Christine  and
      Costa-juss{\`a}, Marta  and
      Sta{\'n}czak, Karolina  and
      Nozza, Debora",
    booktitle = "Proceedings of the 6th Workshop on Gender Bias in Natural Language Processing (GeBNLP)",
    month = aug,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/landing_page/2025.gebnlp-1.6/",
    doi = "10.18653/v1/2025.gebnlp-1.6",
    pages = "75--82",
    ISBN = "979-8-89176-277-0",
    abstract = "Bias in Nepali NLP is rarely addressed, as the language is classified as low-resource, which leads to the perpetuation of biases in downstream systems. Our research focuses on gender bias in Nepali-English machine translation, an area that has seen little exploration. With the emergence of Large Language Models(LLM), there is a unique opportunity to mitigate these biases. In this study, we quantify and evaluate gender bias by constructing an occupation corpus and adapting three gender-bias challenge sets for Nepali. Our findings reveal that gender bias is prevalent in existing translation systems, with translations often reinforcing stereotypes and misrepresenting gender-specific roles. However, LLMs perform significantly better in both gender-neutral and gender-specific contexts, demonstrating less bias compared to traditional machine translation systems. Despite some quirks, LLMs offer a promising alternative for culture-rich, low-resource languages like Nepali. We also explore how LLMs can improve gender accuracy and mitigate biases in occupational terms, providing a more equitable translation experience. Our work contributes to the growing effort to reduce biases in machine translation and highlights the potential of LLMs to address bias in low-resource languages, paving the way for more inclusive and accurate translation systems."
}