Enhancing Accessibility of Medical Texts through Large Language Model-Driven Plain Language Adaptation

2026-09-15

AI Summary

本文通过利用大型语言模型（如GPT-4o-mini等）和混合代理技术，解决医疗文本复杂难以理解的问题，实现自动化的平实语言转换，使医疗信息更易于患者理解。

Abstract

This paper addresses the challenge of making complex healthcare information more accessible through automated Plain Language Adaptation (PLA). PLA aims to simplify technical medical language, bridging a critical gap between the complexity of healthcare texts and patients' reading comprehension. Recent advances in Large Language Models (LLMs), such as GPT and BART, have opened new possibilities for PLA, especially in zero-shot and few-shot learning contexts where task-specific data is limited. In this work, we leverage the capabilities of LLMs such as GPT-4o-mini, Gemini-1.5-pro, and LLaMA for text simplification. Additionally, we incorporate Mixture-of-Agents (MoA) techniques to enhance adaptability and robustness in PLA tasks. Key contributions include a comparative analysis of prompting strategies, finetuning with QLoRA on different LLMs, and the integration of MoA technique. Our findings demonstrate the effectiveness of LLM-driven PLA, showcasing its potential in making healthcare information more comprehensible while preserving essential content.

Problem

• Plain Language Adaptation
• Healthcare Information Accessibility
• Large Language Models

Innovation

• Large Language Models
• Plain Language Adaptation
• Mixture-of-Agents
• QLoRA