Severity-Aware Curriculum Learning with Multi-Model Response Selection for Medical Text Generation

Computer Science > Artificial Intelligence [Submitted on 3 Jun 2026] Severity-Aware Curriculum Learning with Multi-Model Response Selection for Medical Text Generation Ahmed Alansary, Molham Mohamed, Ali Hamdi Telehealth systems have become increasingly important for delivering accessible and timely medical information. Existing large language models often struggle to provide consistent and contextually appropriate medical responses across varying levels of case severity. This limitation highlights the need for models that can effectively adapt to the progressive complexity in medical queries. To address this challenge, we introduce a severity-aware multi-model framework that integrates curriculum training strategy with relevance-based response selection. The proposed framework employs a three-stage curriculum learning strategy, where each model is trained sequentially on mild, moderate, and critical cases to progressively acquire domain knowledge. The approach utilizes five large language models, each independently trained under the same curriculum scheme. During inference, all models generate candidate responses, and the most appropriate response is selected as the final output. The framework is trained and evaluated on the MAQA dataset, which provides annotated medical question-answer pairs. Experimental results evaluated using BERTScore demonstrate that the proposed method achieves superior performance compared to both baseline and fine-tuned models, attaining 86.71% in the baseline setting and 90.30% after fine-tuning. These results highlight the effectiveness of combining curriculum learning with multi-model response selection in improving response quality and relevance in medical text generation. Comments: 6 pages, 3 figures, IMSA2026 Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2606.05510 [cs.AI]   (or arXiv:2606.05510v1 [cs.AI] for this version)   https://doi.org/10.48550/arXiv.2606.05510 Focus to learn more Submission history From: Ahmed Alansary [view email] [v1] Wed, 3 Jun 2026 23:28:34 UTC (400 KB) Access Paper: view license Current browse context: cs.AI < prev   |   next > new | recent | 2026-06 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)