@article{ranjbar-kalahroodi-etal-2026-persianmedqa,
    title = "{P}ersian{M}ed{QA}: Evaluating Large Language Models on a {P}ersian-{E}nglish Bilingual Medical Question Answering Benchmark",
    author = "Ranjbar Kalahroodi, Mohammad Javad  and
      Sheikholselami, Amirhossein  and
      Karimi Arpanahi, Sepehr  and
      Ranjbar Kalahroodi, Sepideh  and
      Faili, Heshaam  and
      Shakery, Azadeh",
    editor = "Piperidis, Stelios  and
      Bel, N{\'u}ria  and
      van den Heuvel, Henk  and
      Ide, Nancy  and
      Krek, Simon  and
      Toral, Antonio",
    journal = "International Conference on Language Resources and Evaluation",
    volume = "main",
    month = may,
    year = "2026",
    address = "Palma de Mallorca, Spain",
    publisher = "ELRA Language Resource Association",
    url = "https://preview.aclanthology.org/ingest-lrec/2026.lrec-main.342/",
    pages = "4371--4386",
    abstract = "Large Language Models (LLMs) have achieved remarkable performance on a wide range of Natural Language Processing (NLP) benchmarks, often surpassing human-level accuracy. However, their reliability in high-stakes domains such as medicine, particularly in low-resource languages, remains underexplored. In this work, we introduce PersianMedQA, a large-scale dataset of 20,785 expert-validated multiple-choice Persian medical questions from 14 years of Iranian national medical exams, spanning 23 medical specialties and designed to evaluate LLMs in both Persian and English. We benchmark 41 state-of-the-art models, including general-purpose, Persian, and medical LLMs, in zero-shot and chain-of-thought (CoT) settings. Our results show that closed-weight general models (e.g., GPT-4.1) consistently outperform all other categories, achieving 83.09{\%} accuracy in Persian and 80.7{\%} in English, while Persian LLMs such as Dorna underperform significantly (e.g., 34.9{\%} in Persian), often struggling with both instruction-following and domain reasoning. We also analyze the impact of translation, showing that while English performance is generally higher, 3-10{\%} of questions can only be answered correctly in Persian due to cultural and clinical contextual cues that are lost in translation. Finally, we demonstrate that model size alone is insufficient for robust performance without strong domain or language adaptation. PersianMedQA provides a foundation for evaluating bilingual and culturally grounded medical reasoning in LLMs. The dataset, along with a bilingual medical dictionary, is publicly available at: https://huggingface.co/datasets/MohammadJRanjbar/PersianMedQA."
}