Understanding the Impact of Long-Term Memory on Self-Disclosure with Large Language Model-Driven Chatbots for Public Health Intervention

arXiv:2402.11353v1 Announce Type: cross
Abstract: 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 …

abstract arxiv chatbots conversations cs.ai cs.cl cs.hc health impact interactions knowledge language language model language models large language large language model large language models llms long-term memory monitoring public public health support through type understanding