用大模型帮老人更清楚表达技术问题，提升求助效率。
Helping the Helper: LLM-Assisted Problem Articulation for Older Adults Seeking Technology Support
- 用大模型分析并重述老人模糊或冗长的技术问题描述。
- 重述后的问题让自动解决方案准确率从35%提升至69%。
- 适合关心老年科技助老、人机交互的研究者和开发者。
老年人因不熟悉技术术语及认知能力变化，常难以清晰表达技术支持需求。我们通过日记研究（n=27）识别出四类沟通障碍：冗长、不完整、过度指定和指定不足。为此，开发了基于大语言模型（LLM）的处理管道，用于澄清上下文并重述非结构化查询。经大模型重述后的查询使自动解决方案准确率显著提升至69%（原为35%）。此外，作为技术支持者的年轻人（n=48）对重述后问题的理解度达93.7%（原为65.8%），且提供支持时感受更轻松。老年用户（n=34）也认为最终解决方案高度可操作（94.7%）。最后，我们构建了首个面向老年人技术求助的合成数据集（STAQ）。本研究证明，大模型能有效缓解老年人在技术求助中的沟通障碍。
原文摘要 · Abstract (English)
Older adults often struggle to articulate technology support needs due to unfamiliar technical terminology and age-related cognitive changes. We explore how large language models (LLMs) can facilitate this problem articulation process. Through a diary study (n = 27), we identified four communication barriers in older adults' queries: verbosity, incompleteness, over-specification, and under-specification. To mitigate these barriers, we developed an LLM pipeline that clarifies context and paraphrases unstructured queries. LLM-rephrased queries significantly improved automated solution accuracy (69% vs. 35%). Furthermore, younger adults (n = 48) acting as technology helpers understood LLM-rephrased queries better (93.7% vs. 65.8%) and reported greater ease in providing support. Older adults (n = 34) also found the resulting solutions highly actionable (94.7%). Finally, we contribute the first synthetic dataset of older adults' technology assistance queries (STAQ). This work demonstrates how LLMs can improve technology support seeking for older adults by addressing age-related communication barriers.
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