Consistent Client Simulation for Motivational Interviewing-based Counseling
This work addresses a domain-specific need for scalable training tools in mental health counseling, though it is incremental as it builds on existing simulation methods.
The paper tackles the problem of simulating human clients for mental health counseling training by ensuring actions align with client profiles and negative behavior settings, and demonstrates that their method achieves higher consistency than previous approaches in evaluations.
Simulating human clients in mental health counseling is crucial for training and evaluating counselors (both human or simulated) in a scalable manner. Nevertheless, past research on client simulation did not focus on complex conversation tasks such as mental health counseling. In these tasks, the challenge is to ensure that the client's actions (i.e., interactions with the counselor) are consistent with with its stipulated profiles and negative behavior settings. In this paper, we propose a novel framework that supports consistent client simulation for mental health counseling. Our framework tracks the mental state of a simulated client, controls its state transitions, and generates for each state behaviors consistent with the client's motivation, beliefs, preferred plan to change, and receptivity. By varying the client profile and receptivity, we demonstrate that consistent simulated clients for different counseling scenarios can be effectively created. Both our automatic and expert evaluations on the generated counseling sessions also show that our client simulation method achieves higher consistency than previous methods.