@inproceedings{scalvini-etal-2025-prompt,
    title = "Prompt Engineering Enhances {Faroese} {MT,} but Only Humans Can Tell",
    author = "Scalvini, Barbara  and
      Simonsen, Annika  and
      Debess, Iben Nyholm  and
      Einarsson, Hafsteinn",
    editor = "Johansson, Richard  and
      Stymne, Sara",
    booktitle = "Proceedings of the Joint 25th Nordic Conference on Computational Linguistics and 11th Baltic Conference on Human Language Technologies (NoDaLiDa/Baltic-HLT 2025)",
    month = mar,
    year = "2025",
    address = "Tallinn, Estonia",
    publisher = "University of Tartu Library",
    url = "https://preview.aclanthology.org/Ingest-2025-COMPUTEL/2025.nodalida-1.63/",
    pages = "622--633",
    ISBN = "978-9908-53-109-0",
    abstract = "This study evaluates GPT-4`s English-to-Faroese translation capabilities, comparing it with multilingual models on FLORES-200 and Sprotin datasets. We propose a prompt optimization strategy using Semantic Textual Similarity (STS) to improve translation quality. Human evaluation confirms the effectiveness of STS-based few-shot example selection, though automated metrics fail to capture these improvements. Our findings advance LLM applications for low-resource language translation while highlighting the need for better evaluation methods in this context."
}