基于 LLM 的医疗文本通俗语言改写

Computer Science > Computation and Language arXiv:2609.17398v1 (cs) [Submitted on 15 Sep 2026] Title:Enhancing Accessibility of Medical Texts through Large Language Model-Driven Plain Language Adaptation Authors:Ting-Wei Chang, Hen-Hsen Huang, Hsin-Hsi Chen View a PDF of the paper titled Enhancing Accessibility of Medical Texts through Large Language Model-Driven Plain Language Adaptation, by Ting-Wei Chang and 2 other authors View PDF HTML (experimental) 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. Comments: Subjects: Computation and Language (cs.CL) Cite as: arXiv:2609.17398 [cs.CL] (or arXiv:2609.17398v1 [cs.CL] for this version) https://doi.org/10.48550/arXiv.2609.17398 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Journal reference: Proceedings of the Thirty-Third Text REtrieval Conference (TREC 2024), NIST Special Publication 1329, 2024 Related DOI: https://doi.org/10.6028/nist.sp.1329plaba-ntu_nlp Focus to learn more DOI(s) linking to related resources Submission history From: Ting-Wei Chang [view email] [v1] Tue, 15 Sep 2026 16:33:38 UTC (211 KB) Full-text links: Access Paper: View a PDF of the paper titled Enhancing Accessibility of Medical Texts through Large Language Model-Driven Plain Language Adaptation, by Ting-Wei Chang and 2 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.CL new | recent | 2026-09 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)