# A Severity-Based Curriculum Learning Strategy for Arabic Medical Text Generation

arXiv: https://arxiv.org/abs/2604.06365 · HTML: https://mlsanity.com/paper/2604.06365/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 18 references that are in the map.
- First posted on arXiv 7 Apr 2026.

## Builds on (most cited first, 10 of 10)

- [Large language models in healthcare and medical domain: A review](https://mlsanity.com/paper/2401.06775/) (2023)
- [Competence-based Multimodal Curriculum Learning for Medical Report Generation](https://mlsanity.com/paper/2206.14579/) (2022)
- [CDL: Curriculum Dual Learning for Emotion-Controllable Response Generation](https://mlsanity.com/paper/2005.00329/) (2020)
- [Fine-Tuning by Curriculum Learning for Non-Autoregressive Neural Machine Translation](https://mlsanity.com/paper/1911.08717/) (2019)
- [Does the Order of Training Samples Matter? Improving Neural Data-to-Text Generation with Curriculum Learning](https://mlsanity.com/paper/2102.03554/) (2021)
- [MedSyn: LLM-based Synthetic Medical Text Generation Framework](https://mlsanity.com/paper/2408.02056/) (2024)
- [A Multi-Layered Large Language Model Framework for Disease Prediction](https://mlsanity.com/paper/2502.00063/) (2025)
- [An Ensemble Classification Approach in A Multi-Layered Large Language Model Framework for Disease Prediction](https://mlsanity.com/paper/2509.02446/) (2025)
- [Arabic Large Language Models for Medical Text Generation](https://mlsanity.com/paper/2509.10095/) (2025)
- [TACL: Threshold-Adaptive Curriculum Learning Strategy for Enhancing Medical Text Understanding](https://mlsanity.com/paper/2510.15269/) (2025)

## Built on by (most cited first, 2 of 2)

- [Severity-Aware Curriculum Learning with Multi-Model Response Selection for Medical Text Generation](https://mlsanity.com/paper/2606.05510/) (2026)
- [A Self Consistency Based Reranking for Narrative Question Answering](https://mlsanity.com/paper/2606.15741/) (2026)

## Abstract (arXiv)

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 dataset is annotated with three severity levels (Mild, Moderate, and Critical) using a rule-based method developed in this study. The results demonstrate that incorporating severity-aware curriculum learning leads to consistent performance improvements across all tested models, with gains of around +4% to +7% over baseline models and +3% to +6% compared with conventional fine-tuning approaches.