用五个角色协作，让医学文本更易懂且不失真。
Society of Medical Simplifiers
- 五种角色分工作业，像团队协作般逐步简化文本。
- 在Cochrane数据集上效果优于或媲美顶尖方法。
- 适合需要准确简化医学内容的研究者与公众读者。
医学文本简化对提升非专业人士理解生物医学文献至关重要。传统方法难以应对医学术语和行话，缺乏动态调整简化过程的灵活性。相比之下，大语言模型（LLMs）通过迭代优化和专业代理间的协作，提供了更强的控制能力。本文提出受“心智社会”（Society of Mind, SOM）哲学启发的医学简化框架——医学简化者社会（Society of Medical Simplifiers）。该框架赋予五个不同角色：普通读者、简化者、医学专家、语言澄清者、冗余检查者，并构建交互循环。各代理协同推进文本简化，同时保持原始内容的复杂性与准确性。在Cochrane文本简化数据集上的评估表明，该框架在可读性和内容保留方面达到或超越现有最佳方法，实现了受控的高质量简化。
原文摘要 · Abstract (English)
Medical text simplification is crucial for making complex biomedical literature more accessible to non-experts. Traditional methods struggle with the specialized terms and jargon of medical texts, lacking the flexibility to adapt the simplification process dynamically. In contrast, recent advancements in large language models (LLMs) present unique opportunities by offering enhanced control over text simplification through iterative refinement and collaboration between specialized agents. In this work, we introduce the Society of Medical Simplifiers, a novel LLM-based framework inspired by the "Society of Mind" (SOM) philosophy. Our approach leverages the strengths of LLMs by assigning five distinct roles, i.e., Layperson, Simplifier, Medical Expert, Language Clarifier, and Redundancy Checker, organized into interaction loops. This structure allows the agents to progressively improve text simplification while maintaining the complexity and accuracy of the original content. Evaluations on the Cochrane text simplification dataset demonstrate that our framework is on par with or outperforms state-of-the-art methods, achieving superior readability and content preservation through controlled simplification processes.
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