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.
@article{arxiv.2401.16167,
title = {"You tell me": A Dataset of GPT-4-Based Behaviour Change Support Conversations},
author = {Selina Meyer and David Elsweiler},
journal= {arXiv preprint arXiv:2401.16167},
year = {2026}
}
Comments
Preprint as accepted at the 2024 ACM SIGIR Conference on Human Information Interaction and Retrieval (CHIIR '24)