用大模型与人工校验，提取荷兰癌症患者访谈和论坛中的隐喻语言。
Dutch Metaphor Extraction from Cancer Patients' Interviews and Forum Data using LLMs and Human in the Loop
- 结合大模型提示工程与人工审核，从双源数据中提取患者隐喻表达。
- 构建首个荷兰语癌症患者隐喻语料库HealthQuote.NL，含数百条经验证的隐喻。
- 适用于医疗沟通研究、个性化护理设计，提升医患共情与健康素养。
隐喻和比喻性语言在医护人员、患者及其家属之间的医疗沟通中起着重要作用。本文聚焦于荷兰语癌症患者数据，利用两类数据源：(1) 癌症患者叙事访谈数据，(2) 在线论坛数据，包括患者的帖子、评论及向专业人士提出的问题。我们探讨当前最先进大型语言模型（LLMs）在此任务上的表现，比较了链式思维推理、少样本学习和自提示等不同提示策略。通过人机协同机制验证所提取的隐喻，并将其整理成名为 HealthQuote.NL 的语料库。我们认为这些提取出的隐喻可支持更优的患者照护，如共享决策、改善医患沟通、提升患者健康素养，并可指导个性化照护路径的设计。相关提示模板与资源已开源至 https://github.com/4dpicture/HealthQuote.NL。
原文摘要 · Abstract (English)
Metaphors and metaphorical language (MLs) play an important role in healthcare communication between clinicians, patients, and patients' family members. In this work, we focus on Dutch language data from cancer patients. We extract metaphors used by patients using two data sources: (1) cancer patient storytelling interview data and (2) online forum data, including patients' posts, comments, and questions to professionals. We investigate how current state-of-the-art large language models (LLMs) perform on this task by exploring different prompting strategies such as chain of thought reasoning, few-shot learning, and self-prompting. With a human-in-the-loop setup, we verify the extracted metaphors and compile the outputs into a corpus named HealthQuote.NL. We believe the extracted metaphors can support better patient care, for example shared decision making, improved communication between patients and clinicians, and enhanced patient health literacy. They can also inform the design of personalized care pathways. We share prompts and related resources at https://github.com/4dpicture/HealthQuote.NL
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