Client Simulation and Topic Exploration: Lessons learned from Developing Conversational Counselling Agents
Client Simulation and Topic Exploration: Lessons learned from Developing Conversational Counselling Agents
Conversational counsellor agents have become essential tools for addressing the rising demand for scalable and accessible mental health support. In this talk, I will share some recent work with my collaborators on building LLM-based conversational counselling agents grounded in Motivational Interviewing (MI) -- a client-centred counselling approach designed to address ambivalence and facilitate behaviour change.
Our framework consists of client's state inference, motivation topic exploration, and response generation modules. We also propose a consistent client simulation module to assist the development of the counsellor agents.
Results show that our framework not only outperforms several state-of-the-art methods but also shows more realistic counsellor-like behaviour.
Speaker
Jing Jiang
Jing Jiang is a Professor in the School of Computing at the Australian National University. Previously she was a Professor and Director of the AI & Data Science Cluster in the School of Computing and Information Systems at the Singapore Management University. Her research interests include natural language processing, text mining, and machine learning. She has received two test-of-time awards for her work on social media analysis, and she was named Singapore’s 100 Women in Tech in 2021. She holds a PhD degree in Computer Science from the University of Illinois Urbana-Champaign.
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