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通过大型语言模型驱动的简明语言改编提升医学文本的可及性
该论文研究自动化简明语言改编（PLA），旨在将复杂医学文本简化为患者更易理解的内容。作者使用 GPT-4o-mini、Gemini-1.5-pro 和 LLaMA 等 LLM，并引入 Mixture-of-Agents（MoA）提升适应性与鲁棒性，主要贡献包括比较不同提示策略、用 QLoRA 微调不同 LLM 以及集成 MoA。摘要称结果显示了 LLM 驱动 PLA 的有效性，但未提供具体量化指标。
AI推荐理由 arXiv 论文，来源可信、摘要明确，提出用 GPT-4o-mini、Gemini-1.5-pro、LLaMA 及 Mixture-of-Agents 做医学文本简明化（PLA），并比较提示策略与 QLoRA 微调。属应用型研究，方法组合有一定参考价值，但摘要未给出量化结果，且 PLA 方向已较成熟，对 AI 核心技术与产业落地的增量有限。
62
正文
Computer Science > Computation and Language
arXiv:2609.17398 (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
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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.
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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
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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
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From: Ting-Wei Chang [view email]
[v1]
Tue, 15 Sep 2026 16:33:38 UTC (211 KB)
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