[2604.06365] A Severity-Based Curriculum Learning Strategy for Arabic Medical Text Generation

Computer Science > Computation and Language arXiv:2604.06365 (cs) [Submitted on 7 Apr 2026] Title:A Severity-Based Curriculum Learning Strategy for Arabic Medical Text Generation Authors:Ahmed Alansary, Molham Mohamed, Ali Hamdi View a PDF of the paper titled A Severity-Based Curriculum Learning Strategy for Arabic Medical Text Generation, by Ahmed Alansary and 2 other authors View PDF HTML (experimental) Abstract:Arabic medical text generation is increasingly needed to help users interpret symptoms and access general health guidance in their native language. Nevertheless, many existing methods assume uniform importance across training samples, overlooking differences in clinical severity. This simplification can hinder the model's ability to properly capture complex or high-risk cases. To overcome this issue, this work introduces a Severity-based Curriculum Learning Strategy for Arabic Medical Text Generation, where the training process is structured to move gradually from less severe to more critical medical conditions. The approach divides the dataset into ordered stages based on severity and incrementally exposes the model to more challenging cases during fine-tuning, allowing it to first learn basic medical patterns before addressing more complex scenarios. The proposed method is evaluated on a subset of the Medical Arabic Question Answering (MAQA) dataset, which includes Arabic medical questions describing symptoms alongside corresponding responses. In addition, the ...