Peter Chang
translateEN
All publications

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

Ting-Wei Chang, Hen-Hsen Huang, Hsin-Hsi Chen

The Thirty-Third Text REtrieval Conference · PLABA track · 2024

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.

TREC 2024 · PLABA

TREC 2024 · PLABAFirst author

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

Ting-Wei Chang, Hen-Hsen Huang, Hsin-Hsi Chen

TL;DRRewrites dense medical text into language patients can actually read.

arXivDOINIST SP 1329PDF
BibTeX
@inproceedings{chang2024enhancing,
  title     = {Enhancing Accessibility of Medical Texts through Large Language Model-Driven Plain Language Adaptation},
  author    = {Chang, Ting-Wei and Huang, Hen-Hsen and Chen, Hsin-Hsi},
  booktitle = {Proceedings of the Thirty-Third Text REtrieval Conference ({TREC} 2024)},
  series    = {NIST Special Publication 1329},
  year      = {2024},
  doi       = {10.6028/nist.sp.1329plaba-ntu_nlp},
  eprint    = {2609.17398},
  archivePrefix = {arXiv},
  primaryClass  = {cs.CL},
  url       = {https://arxiv.org/abs/2609.17398},
  note      = {PLABA track}
}