Understanding the Impact of Long-Term Memory on Self-Disclosure with
  Large Language Model-Driven Chatbots for Public Health Intervention
Eunkyung Jo Yuin Jeong SoHyun Park Daniel A. Epstein Young-Ho Kim
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.