A Severity-Based Curriculum Learning Strategy for Arabic Medical Text Generation
arXiv:2604.06365v1 Announce Type: new 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
arXiv:2604.06365v1 Announce Type: new 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 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.
Executive Summary
This article proposes a novel Severity-based Curriculum Learning Strategy for Arabic Medical Text Generation, addressing the critical need for accurate health information in Arabic. The core innovation lies in structuring the model's training process to progress from less severe to more critical medical conditions, a departure from conventional uniform training. By dividing a subset of the MAQA dataset into three severity levels (Mild, Moderate, Critical) using a newly developed rule-based method, the authors demonstrate that this incremental exposure to challenging cases significantly improves model performance. The strategy yielded consistent gains of +4% to +7% over baselines and +3% to +6% over standard fine-tuning, highlighting its potential to enhance the reliability of AI-generated medical advice in sensitive domains.
Key Points
- ▸ Introduces a Severity-based Curriculum Learning Strategy for Arabic Medical Text Generation.
- ▸ Addresses the gap of uniform importance assumption in existing methods, particularly for complex or high-risk medical cases.
- ▸ Proposes a staged training approach, gradually exposing the model to medical conditions from 'Mild' to 'Critical' severity.
- ▸ Develops a rule-based method for annotating medical text with three severity levels (Mild, Moderate, Critical).
- ▸ Achieves notable performance improvements (4-7% over baselines, 3-6% over conventional fine-tuning) on a subset of the MAQA dataset.
Merits
Novelty and Domain Specificity
The application of curriculum learning tailored to clinical severity in Arabic medical text generation is a significant and novel contribution, directly addressing a critical need in a specialized domain.
Improved Performance
The demonstrated performance gains are substantial and consistent across models, suggesting the strategy's robustness and effectiveness in enhancing generation quality.
Addressing Clinical Nuance
By explicitly accounting for severity, the method moves beyond simplistic text generation to potentially capture the nuanced importance of different medical conditions, crucial for safety and accuracy.
Dataset Annotation Contribution
The development of a rule-based severity annotation method for Arabic medical text is a valuable contribution, potentially facilitating future research and dataset development in this under-resourced language.
Demerits
Limited Dataset Scope
The evaluation is performed on 'a subset' of the MAQA dataset. The generalizability of the findings might be constrained without testing on a larger, more diverse, and independently curated medical dataset.
Subjectivity of Severity Annotation
While rule-based, the criteria for 'Mild,' 'Moderate,' and 'Critical' severity can be inherently subjective and context-dependent in medicine. The robustness and inter-annotator agreement (if human validation was involved) of this rule-based method warrant further scrutiny and validation.
Lack of Human Evaluation
The abstract does not mention human evaluation of the generated text quality, which is paramount in medical contexts where accuracy, safety, and appropriateness are critical beyond automated metrics.
Computational Overhead
Curriculum learning strategies can sometimes introduce additional complexity and computational overhead. The abstract doesn't detail the training efficiency or resource implications of the staged approach.
Expert Commentary
This paper presents a compelling and timely intervention in the domain of Arabic medical text generation, addressing a critical gap in existing methodologies. The Severity-based Curriculum Learning Strategy is conceptually elegant and empirically effective, demonstrating a clear understanding of the hierarchical nature of medical conditions. The performance gains are significant, suggesting that explicitly encoding clinical importance during training is not merely an incremental improvement but a foundational shift towards more responsible and reliable AI in healthcare. However, the true litmus test for such a system lies not just in automated metrics but in its clinical utility and safety. The reliance on a rule-based severity annotation, while a practical initial step, necessitates rigorous external validation and potentially human expert consensus to mitigate inherent subjectivity. Future work must prioritize comprehensive human evaluation of the generated outputs, particularly for 'critical' cases, to assess clinical accuracy, appropriateness, and potential for harm. The ethical implications of deploying such systems, even with improved performance, demand careful consideration and robust regulatory oversight.
Recommendations
- ✓ Conduct extensive human evaluation by medical professionals to validate the clinical accuracy, safety, and appropriateness of the generated text, especially for moderate and critical severity levels.
- ✓ Expand the evaluation to larger, more diverse, and independently validated Arabic medical datasets to confirm the generalizability and robustness of the proposed curriculum learning strategy.
- ✓ Provide a detailed analysis of the rule-based severity annotation method, including inter-annotator agreement (if applicable) and a discussion of its limitations and potential biases.
- ✓ Investigate the computational efficiency and resource implications of the curriculum learning approach compared to traditional fine-tuning, particularly for deployment in resource-constrained environments.
- ✓ Explore methods for incorporating explainability into the severity classification and text generation process, enhancing transparency and trust for medical users.
Sources
Original: arXiv - cs.CL