collaborators

6 papers

cs.CL2026

Bridging the English-Arabic Medical Knowledge Gap: Targeted Low-Rank Adaptation via Causal Layer Selection

Chaimae Abouzahir, Musa Khan, Hala Ali-Hassan +7

Large Language Models (LLMs) perform strongly in English medical tasks but degrade substantially in Arabic, a gap widely attributed to limited training data. We systematically inve…

cs.CL2026

MedGuards: Multi-Agent System for Reliable Medical Error Detection and Correction

Congbo Ma, Hu Wang, Yichun Zhang +1

As Large Language Models (LLMs) are increasingly deployed in healthcare settings, accurate error detection and correction in generated or existing text becomes critical, as even mi…

cs.CL2026

MedErrBench: A Fine-Grained Multilingual Benchmark for Medical Error Detection and Correction with Clinical Expert Annotations

Congbo Ma, Yichun Zhang, Yousef Al-Jazzazi +6

Inaccuracies in existing or generated clinical text may lead to serious adverse consequences, especially if it is a misdiagnosis or incorrect treatment suggestion. With Large Langu…

cs.CL2026

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

Chaimae Abouzahir, Congbo Ma, Nizar Habash +1

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.…

cs.CL2026

MedAraBench: Large-Scale Arabic Medical Question Answering Dataset and Benchmark

Mouath Abu-Daoud, Leen Kharouf, Omar El Hajj +6

Arabic remains one of the most underrepresented languages in natural language processing research, particularly in medical applications, due to the limited availability of open-sou…

cs.CL2025

AraHealthQA 2025: The First Shared Task on Arabic Health Question Answering

Hassan Alhuzali, Walid Al-Eisawi, Muhammad Abdul-Mageed +9

We introduce AraHealthQA 2025, the Comprehensive Arabic Health Question Answering Shared Task, held in conjunction with ArabicNLP 2025 (co-located with EMNLP 2025). This shared tas…