@inproceedings{yang-etal-2025-consistent,
title = "Consistent Client Simulation for Motivational Interviewing-based Counseling",
author = "Yang, Yizhe and
Achananuparp, Palakorn and
Huang, Heyan and
Jiang, Jing and
Lim, Nicholas Gabriel and
Ern, Cameron Tan Shi and
Kit, Phey Ling and
Xiuhui, Jenny Giam and
Pinto, John and
Lim, Ee-Peng",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.acl-long.1021/",
doi = "10.18653/v1/2025.acl-long.1021",
pages = "20959--20998",
ISBN = "979-8-89176-251-0",
abstract = "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."
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<abstract>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.</abstract>
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%0 Conference Proceedings
%T Consistent Client Simulation for Motivational Interviewing-based Counseling
%A Yang, Yizhe
%A Achananuparp, Palakorn
%A Huang, Heyan
%A Jiang, Jing
%A Lim, Nicholas Gabriel
%A Ern, Cameron Tan Shi
%A Kit, Phey Ling
%A Xiuhui, Jenny Giam
%A Pinto, John
%A Lim, Ee-Peng
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-251-0
%F yang-etal-2025-consistent
%X 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.
%R 10.18653/v1/2025.acl-long.1021
%U https://aclanthology.org/2025.acl-long.1021/
%U https://doi.org/10.18653/v1/2025.acl-long.1021
%P 20959-20998
Markdown (Informal)
[Consistent Client Simulation for Motivational Interviewing-based Counseling](https://aclanthology.org/2025.acl-long.1021/) (Yang et al., ACL 2025)
ACL
- Yizhe Yang, Palakorn Achananuparp, Heyan Huang, Jing Jiang, Nicholas Gabriel Lim, Cameron Tan Shi Ern, Phey Ling Kit, Jenny Giam Xiuhui, John Pinto, and Ee-Peng Lim. 2025. Consistent Client Simulation for Motivational Interviewing-based Counseling. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 20959–20998, Vienna, Austria. Association for Computational Linguistics.