@inproceedings{adegbehingbe-etal-2025-beyond,
    title = "Beyond Generalization :Evaluating Multilingual {LLM}s for {Y}or{\`u}b{\'a} Animal Health Translation",
    author = "Adegbehingbe, Godwin  and
      Soronnadi, Anthony  and
      Adebara, Ife  and
      Adekanmbi, Olubayo",
    editor = "Lignos, Constantine  and
      Abdulmumin, Idris  and
      Adelani, David",
    booktitle = "Proceedings of the Sixth Workshop on African Natural Language Processing (AfricaNLP 2025)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/acl25-workshop-ingestion/2025.africanlp-1.28/",
    pages = "192--194",
    ISBN = "979-8-89176-257-2",
    abstract = "Machine translation (MT) has advanced significantly for high-resource languages, yet specialized domain translation remains a challenge for low-resource languages. This study evaluates the ability of state-of-the-art multilingual models to translate animal health reports from English to Yor{\`u}b{\'a}, a crucial task for veterinary communication in underserved regions. We curated a dataset of 1,468 parallel sentences and compared multiple MT models in zero-shot and fine-tuned settings. Our findings indicate substantial limitations in their ability to generalize to domain-specific translation, with common errors arising from vocabulary mismatch, training data scarcity, and morphological complexity. Fine-tuning improves performance, particularly for the NLLB 3.3B model, but challenges remain in preserving technical accuracy. These results underscore the need for more targeted approaches to multilingual and culturally aware LLMs for African languages."
}