理解长期记忆对基于大型语言模型驱动的公共健康干预聊天机器人中自我披露的影响
Understanding the Impact of Long-Term Memory on Self-Disclosure with Large Language Model-Driven Chatbots for Public Health Intervention
AI总结：
本研究探讨了长期记忆（LTM）对LLM驱动的公共健康聊天机器人自我披露的影响，通过分析CareCall的通话记录与用户访谈，发现LTM能增强健康披露但也面临慢性病和隐私挑战，为LTM整合提供了设计考量。
摘要：
近期的大型语言模型（LLMs）有望通过开放式对话促进健康披露，从而支持公共健康监测，但很少能在重复交互中保留所获取的个体知识。为LLMs增强长期记忆（LTM）提供了提升用户参与度和自我披露的机会，但我们尚不清楚LTM如何影响人们在公共健康干预中与LLM驱动的聊天机器人的交互。我们通过分析1,252条通话记录和对九名用户的访谈，考察了CareCall这一具备LTM的LLM驱动语音聊天机器人的案例。我们发现，LTM通过提供熟悉感增强了健康披露，并促进了对聊天机器人的积极认知。然而，我们也观察到通过LTM促进自我披露面临的挑战，特别是在应对慢性健康状况和隐私担忧方面。我们讨论了在用于公共健康监测的LLM驱动聊天机器人中整合LTM的考量因素，包括根据公共健康目标谨慎决定需要记住哪些话题。
英文摘要：
Recent large language models (LLMs) offer the potential to support public health monitoring by facilitating health disclosure through open-ended conversations but rarely preserve the knowledge gained about individuals across repeated interactions. Augmenting LLMs with long-term memory (LTM) presents an opportunity to improve engagement and self-disclosure, but we lack an understanding of how LTM impacts people's interaction with LLM-driven chatbots in public health interventions. We examine the case of CareCall -- an LLM-driven voice chatbot with LTM -- through the analysis of 1,252 call logs and interviews with nine users. We found that LTM enhanced health disclosure and fostered positive perceptions of the chatbot by offering familiarity. However, we also observed challenges in promoting self-disclosure through LTM, particularly around addressing chronic health conditions and privacy concerns. We discuss considerations for LTM integration in LLM-driven chatbots for public health monitoring, including carefully deciding what topics need to be remembered in light of public health goals.
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