"You tell me": A Dataset of GPT-4-Based Behaviour Change Support Conversations
arXiv:2401.16167 · doi:10.1145/3627508.3638330
Abstract
Conversational agents are increasingly used to address emotional needs on top of information needs. One use case of increasing interest are counselling-style mental health and behaviour change interventions, with large language model (LLM)-based approaches becoming more popular. Research in this context so far has been largely system-focused, foregoing the aspect of user behaviour and the impact this can have on LLM-generated texts. To address this issue, we share a dataset containing text-based user interactions related to behaviour change with two GPT-4-based conversational agents collected in a preregistered user study. This dataset includes conversation data, user language analysis, perception measures, and user feedback for LLM-generated turns, and can offer valuable insights to inform the design of such systems based on real interactions.
Preprint as accepted at the 2024 ACM SIGIR Conference on Human Information Interaction and Retrieval (CHIIR '24)