@inproceedings{abouzahir-etal-2026-cross,
    title = "Cross-Lingual Empirical Evaluation of Large Language Models for {A}rabic Medical Tasks",
    author = "Abouzahir, Chaimae  and
      Ma, Congbo  and
      Habash, Nizar  and
      Shamout, Farah E.",
    editor = {Danilova, Vera  and
      Kurfal{\i}, Murathan  and
      S{\"o}derfeldt, Ylva  and
      Reed, Julia  and
      Burchell, Andrew},
    booktitle = "Proceedings of the 1st Workshop on Linguistic Analysis for Health ({H}ea{L}ing 2026)",
    month = mar,
    year = "2026",
    address = "Rabat, Morocco",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-eacl/2026.healing-1.13/",
    pages = "158--171",
    ISBN = "979-8-89176-367-8",
    abstract = "In recent years, Large Language Models (LLMs) have become widely used in medical applications, such as clinical decision support, medical education and medical question answering. Yet, these models are often English-centric, limiting their robustness and reliability for linguistically diverse communities. Recent work has highlighted discrepancies in performance in low-resource languages for various medical tasks, but the underlying causes remain poorly understood. In this study, we conduct a cross-lingual empirical analysis of LLM performance on Arabic {\&} English medical question and answering. Our findings reveal a persistent language-driven performance gap that intensifies with increasing task complexity. Tokenization analysis exposes structural fragmentation in Arabic medical text, while reliability analysis shows that model-reported confidence and explanations are poor indicators of correctness. Together, these findings underscore the need for language-aware design and evaluation strategies in LLMs for medical tasks."
}