Enhancing Accessibility of Medical Texts through Large Language Model-Driven Plain Language Adaptation
Study shows LLMs with Mixture-of-Agents and QLoRA finetuning effectively simplify medical texts into plain language while preserving content.
The paper evaluates Plain Language Adaptation (PLA) using GPT-4o-mini, Gemini-1.5-pro, and LLaMA in zero-shot and few-shot settings. It compares prompting strategies, QLoRA finetuning across models, and integrates Mixture-of-Agents (MoA) techniques for robustness. Results demonstrate LLM-driven PLA makes healthcare texts more comprehensible while retaining essential content.
- Evaluates GPT-4o-mini, Gemini-1.5-pro, and LLaMA for medical text simplification
- Combines prompting strategies, QLoRA finetuning, and Mixture-of-Agents
- Targets gap between medical text complexity and patient reading comprehension
From the original article
1 min read at arxiv.orgThis 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.
By Ting-Wei Chang · arxiv.org
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