Cross-Lingual Empirical Evaluation of Large Language Models for Arabic Medical Tasks

• URL: http://arxiv.org/abs/2602.05374v1
• Date: Thu, 05 Feb 2026 06:52:46 GMT
• Title: Cross-Lingual Empirical Evaluation of Large Language Models for Arabic Medical Tasks
• Authors: Chaimae Abouzahir, Congbo Ma, Nizar Habash, Farah E. Shamout,
• Abstract summary: Large Language Models (LLMs) have become widely used in medical applications, such as clinical decision support, medical education, and medical question answering.<n>Yet, these models are often English-centric, limiting their robustness and reliability for linguistically diverse communities.<n>Recent work has highlighted discrepancies in performance in low-resource languages for various medical tasks, but the underlying causes remain poorly understood.
• Score: 12.886024273517556
• License: http://creativecommons.org/licenses/by/4.0/
• 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 and 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 suggests that model-reported confidence and explanations exhibit limited correlation with correctness. Together, these findings underscore the need for language-aware design and evaluation strategies in LLMs for medical tasks.

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