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

2026-06-06 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Medical Devices & Health Technology, Clinical Care & Medical Practice · Depth: Expert, quick

Summary

A new severity-aware multi-model framework addresses the challenge of large language models struggling to provide consistent, contextually appropriate medical responses across varying case severities in telehealth systems. This framework integrates a three-stage curriculum training strategy with relevance-based response selection. Each of the five large language models within the framework is independently trained sequentially on mild, moderate, and critical medical cases to progressively acquire domain knowledge. During inference, all five models generate candidate responses, and the most appropriate one is selected as the final output. Evaluated on the MAQA dataset, the proposed method achieved superior BERTScore performance, reaching 86.71% in the baseline setting and 90.30% after fine-tuning, demonstrating the effectiveness of its combined approach for improving medical text generation quality and relevance.

Key takeaway

For Machine Learning Engineers developing telehealth systems, if you are struggling with consistent and contextually appropriate medical responses, consider implementing a severity-aware multi-model framework. This approach, combining curriculum learning across mild, moderate, and critical cases with relevance-based response selection from an ensemble of models, can significantly enhance your system's output quality. You should explore multi-stage training and response selection mechanisms to improve domain adaptation and reliability.

Key insights

Severity-aware curriculum learning with multi-model response selection significantly improves medical text generation quality and relevance.

Principles

• Progressive training on severity improves domain adaptation.
• Ensemble of models enhances response quality and relevance.
• Contextual relevance is key for medical text generation.

Method

A three-stage curriculum trains five independent LLMs sequentially on mild, moderate, then critical medical cases. Inference selects the best response from all models.

In practice

• Implement multi-stage training for domain-specific LLMs.
• Use an ensemble of LLMs for critical response generation.
• Prioritize relevance-based selection for final outputs.

Topics

• Severity-Aware Curriculum Learning
• Multi-Model Response Selection
• Medical Text Generation
• Large Language Models
• Telehealth Systems
• MAQA Dataset

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, NLP Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.