CAMI's combination of client-state inference and topic-tree exploration improves motivational-interviewing counseling performance over four LLM-based baselines in simulated sessions.
Virtual Agents for Alcohol Use Counseling: Exploring LLM-Powered Motivational Interviewing
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abstract
We introduce a novel application of large language models (LLMs) in developing a virtual counselor capable of conducting motivational interviewing (MI) for alcohol use counseling. Access to effective counseling remains limited, particularly for substance abuse, and virtual agents offer a promising solution by leveraging LLM capabilities to simulate nuanced communication techniques inherent in MI. Our approach combines prompt engineering and integration into a user-friendly virtual platform to facilitate realistic, empathetic interactions. We evaluate the effectiveness of our virtual agent through a series of studies focusing on replicating MI techniques and human counselor dialog. Initial findings suggest that our LLM-powered virtual agent matches human counselors' empathetic and adaptive conversational skills, presenting a significant step forward in virtual health counseling and providing insights into the design and implementation of LLM-based therapeutic interactions.
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CAMI: A Counselor Agent Supporting Motivational Interviewing through State Inference and Topic Exploration
CAMI's combination of client-state inference and topic-tree exploration improves motivational-interviewing counseling performance over four LLM-based baselines in simulated sessions.