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    <abstract>The development and benchmarking of Large Language Model (LLM)-based Motivational Interviewing (MI) counsellors now often rely on LLM-based simulated clients. Prior work on simulated clients, however, has not aligned with the specific *tasks* fundamental to the MI therapy approach. A key task is *evoking*, in which the counsellor first elicits the client’s ambivalence and then strengthens the client’s motivation for change. We present **Evoke-Sim**, a task-aware, multi-stage LLM-based client simulation framework for evaluating MI counsellors in smoking cessation, designed specifically for the *evoking* MI task. Evoke-Sim employs structured client profiles, an evoking-specific three-stage conversation flow, and a *reveal* policy that regulates which client profile information might be disclosed at each stage. We show that, compared to existing profile-grounded simulated clients, Evoke-Sim better differentiates counsellors’ task-aware performance under our explicit formalization of the *evoking* task, while reducing non-grounded client statements and premature disclosure of client information.</abstract>
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%0 Conference Proceedings
%T Evaluation of Motivational Interviewing Counsellors with Task-Aware Multi-Stage LLM-Based Simulated Clients
%A Zhu, Jiading
%A Wang, Xinyu Cindy
%A Nguyen, Thomas
%A Lee, Yan Qing
%A Melamed, Osnat C.
%A Selby, Peter
%A Rose, Jonathan
%Y Sousa-Silva, Rui
%Y Lopes Cardoso, Henrique
%Y Koponen, Maarit
%Y Pareja-Lora, Antonio
%S Proceedings of the 3rd Workshop on Language Understanding in the Human-Machine Era (LUHME 2026)
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-471-2
%F zhu-etal-2026-evaluation
%X The development and benchmarking of Large Language Model (LLM)-based Motivational Interviewing (MI) counsellors now often rely on LLM-based simulated clients. Prior work on simulated clients, however, has not aligned with the specific *tasks* fundamental to the MI therapy approach. A key task is *evoking*, in which the counsellor first elicits the client’s ambivalence and then strengthens the client’s motivation for change. We present **Evoke-Sim**, a task-aware, multi-stage LLM-based client simulation framework for evaluating MI counsellors in smoking cessation, designed specifically for the *evoking* MI task. Evoke-Sim employs structured client profiles, an evoking-specific three-stage conversation flow, and a *reveal* policy that regulates which client profile information might be disclosed at each stage. We show that, compared to existing profile-grounded simulated clients, Evoke-Sim better differentiates counsellors’ task-aware performance under our explicit formalization of the *evoking* task, while reducing non-grounded client statements and premature disclosure of client information.
%U https://aclanthology.org/2026.luhme-1.23/
%P 304-359

Markdown (Informal)

[Evaluation of Motivational Interviewing Counsellors with Task-Aware Multi-Stage LLM-Based Simulated Clients](https://aclanthology.org/2026.luhme-1.23/) (Zhu et al., LUHME 2026)

• Evaluation of Motivational Interviewing Counsellors with Task-Aware Multi-Stage LLM-Based Simulated Clients (Zhu et al., LUHME 2026)

ACL

• Jiading Zhu, Xinyu Cindy Wang, Thomas Nguyen, Yan Qing Lee, Osnat C. Melamed, Peter Selby, and Jonathan Rose. 2026. Evaluation of Motivational Interviewing Counsellors with Task-Aware Multi-Stage LLM-Based Simulated Clients. In Proceedings of the 3rd Workshop on Language Understanding in the Human-Machine Era (LUHME 2026), pages 304–359, Budapest, Hungary. Association for Computational Linguistics.