在神话与隐喻之间：重新审视保守语境下用于性与生殖健康的LLM
Between Myths and Metaphors: Rethinking LLMs for SRH in Conservative Contexts
AI总结：
本文研究保守语境下性与生殖健康沟通的间接语言对LLM的挑战，在巴基斯坦开展两阶段研究，分析医患沟通数据并评估五种LLM，发现其在语义漂移和一词多义上表现不佳，并提出了分类框架与设计建议。
摘要：
低资源国家占孕产妇死亡的90%以上，2023年巴基斯坦是贡献近半数死亡的四大国家之一。由于这些死亡大多可预防，大语言模型（LLMs）可通过自动化健康沟通和风险评估来帮助应对此危机。然而，保守语境下的性与生殖健康（SRH）沟通常依赖掩盖含义的间接语言，使基于LLM的干预复杂化。我们在巴基斯坦开展两阶段研究：(1)分析来自临床观察、访谈及医患焦点小组的数据，(2)评估五种流行LLM在此数据上的解释能力。分析识别出沟通的两个轴（指称域和表达方式），并表明LLM在临床交互中难以应对语义漂移、神话和一词多义。我们贡献：(1)SRH沟通的经验主题，(2)间接沟通的分类框架，(3)LLM性能评估，(4)文化情境化SRH沟通的设计建议。
英文摘要：
Low-resource countries represent over 90% of maternal deaths, with Pakistan among the top four countries contributing nearly half in 2023. Since these deaths are mostly preventable, large language models (LLMs) can help address this crisis by automating health communication and risk assessment. However, sexual and reproductive health (SRH) communication in conservative contexts often relies on indirect language that obscures meaning, complicating LLM-based interventions. We conduct a two-stage study in Pakistan: (1) analyzing data from clinical observations, interviews, and focus groups with clinicians and patients, and (2) evaluating the interpretive capabilities of five popular LLMs on this data. Our analysis identifies two axes of communication (referential domain and expression approach) and shows LLMs struggle with semantic drift, myths, and polysemy in clinical interactions. We contribute: (1) empirical themes in SRH communication, (2) a categorization framework for indirect communication, (3) evaluation of LLM performance, and (4) design recommendations for culturally-situated SRH communication.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
