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

arXiv:2609.17398 · cs.CL · Submitted 2026-09-15 · Read on arXiv

cs.CL

Submitted: 2026-09-15

Updated: 2026-09-15

Comments: 10 pages, 3 figures, 6 tables. Published in the Proceedings of the Thirty-Third Text REtrieval Conference (TREC 2024), Plain Language Adaptation of Biomedical Abstracts (PLABA) track

Journal ref: Proceedings of the Thirty-Third Text REtrieval Conference (TREC 2024), NIST Special Publication 1329, 2024

DOI: 10.6028/nist.sp.1329plaba-ntu_nlp

License: http://creativecommons.org/licenses/by/4.0/

The gist: This paper addresses the challenge of making complex healthcare information more accessible through automated Plain Language Adaptation (PLA).

Terminology

Adaptation — 16× in this paper · explained in 1 episode
Accuracy — 12× in this paper · explained in 1 episode
Semantic Similarity — 9× in this paper · explained in 2 episodes
Plain Language Adaptation — 8× in this paper
F1 Score — 5× in this paper · explained in 3 episodes
Natural Language Processing — 5× in this paper
Consistency — 4× in this paper · explained in 3 episodes
LLM judge — 4× in this paper · explained in 2 episodes
Adaptability — 3× in this paper · explained in 1 episode
Alignment — 3× in this paper · explained in 1 episode
Few-Shot Learning — 3× in this paper · explained in 4 episodes
Robustness — 3× in this paper · explained in 5 episodes
Benchmark — 2× in this paper · explained in 2 episodes
Dale-Chall Readability Score — 2× in this paper
Embeddings — 2× in this paper · explained in 2 episodes
Flesch-Kincaid Grade Level — 2× in this paper
In-Context Learning — 2× in this paper · explained in 4 episodes
In-Context Learning (ICL) — 2× in this paper · explained in 5 episodes
Large Language Models (LLMs) — 2× in this paper · explained in 6 episodes
Coleman-Laiu Index — 1× in this paper
Distillation — 1× in this paper · explained in 2 episodes
Generalizability — 1× in this paper · explained in 6 episodes
Generative Model — 1× in this paper · explained in 2 episodes
Generative Models — 1× in this paper · explained in 2 episodes
Knowledge Distillation — 1× in this paper · explained in 11 episodes
Transformer — 1× in this paper · explained in 2 episodes
alpha — 1× in this paper · explained in 1 episode

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.

Sources

Automated Lay Language Summarization of Biomedical Scientific Reviews

Terminology

Abstract

Sources

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