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.

这篇研究报告介绍了一个包含用户与基于 GPT-4 的对话代理交互数据的数据集，用于探索行为改变的相关话题。该数据集提供了对话数据、用户语言分析、认知度量以及对基于大型语言模型生成的文本的用户反馈，可为基于真实交互设计此类系统提供有价值的见解。