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.

翻译：近期的大型语言模型（LLMs）通过开放式对话促进健康信息披露，从而为支持公共卫生监测提供了潜力，但很少能跨多次交互保留所获取的个体知识。为LLMs增强长时记忆（LTM）提供了改善用户参与度和自我披露的机会，但我们尚不清楚LTM如何影响人们与LLM驱动的聊天机器人在公共卫生干预中的互动。我们通过分析1,252条通话记录和九名用户的访谈，研究了CareCall——一个具有LTM的LLM驱动的语音聊天机器人——的案例。我们发现，LTM通过提供熟悉感增强了健康信息披露，并促进了对聊天机器人的积极看法。然而，我们也观察到通过LTM促进自我披露的挑战，特别是在处理慢性健康状况和隐私问题方面。我们讨论了在LLM驱动的聊天机器人中整合LTM以用于公共卫生监测的考虑因素，包括根据公共卫生目标仔细决定哪些话题需要被记住。