Severity-Aware Weighted Loss for Arabic Medical Text Generation
Large language models have shown strong potential for Arabic medical text generation; however, traditional fine-tuning objectives treat all medical cases uniformly, ignoring differences in clinical severity. This limitation is particularly critical in healthcare settings, where errors in severe cases contain higher clinical risk. In this work, we propose a severity-aware weighted loss for fine-tuning Arabic language models on medical complaint-response data. The method depends on soft severity probabilities to dynamically scale token-level loss contributions during optimization, thereby prioritizing clinically critical interactions without modifying model architectures. Experiments are co...
| Authors | Ahmed Alansary, Molham Mohamed, Ali Hamdi |
|---|---|
| Primary category | cs.CL |
| Published | 7 April 2026 |
| Last updated at source | 7 April 2026 |
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