# Consistent Client Simulation for Motivational Interviewing-based Counseling

arXiv: https://arxiv.org/abs/2502.02802 · HTML: https://mlsanity.com/paper/2502.02802/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 26 references that are in the map.
- First posted on arXiv 5 Feb 2025.

## Builds on (most cited first, 12 of 12)

- [Efficient Memory Management for Large Language Model Serving with PagedAttention](https://mlsanity.com/paper/2309.06180/) (2023)
- [OpenAssistant Conversations -- Democratizing Large Language Model Alignment](https://mlsanity.com/paper/2304.07327/) (2023)
- [Large Language Model Alignment: A Survey](https://mlsanity.com/paper/2309.15025/) (2023)
- [Prompting and Evaluating Large Language Models for Proactive Dialogues: Clarification, Target-guided, and Non-collaboration](https://mlsanity.com/paper/2305.13626/) (2023)
- [Plug-and-Play Policy Planner for Large Language Model Powered Dialogue Agents](https://mlsanity.com/paper/2311.00262/) (2023)
- [A Computational Framework for Behavioral Assessment of LLM Therapists](https://mlsanity.com/paper/2401.00820/) (2024)
- [CharacterChat: Learning towards Conversational AI with Personalized Social Support](https://mlsanity.com/paper/2308.10278/) (2023)
- [Can Large Language Models be Good Emotional Supporter? Mitigating Preference Bias on Emotional Support Conversation](https://mlsanity.com/paper/2402.13211/) (2024)
- [Automatic Interactive Evaluation for Large Language Models with State Aware Patient Simulator](https://mlsanity.com/paper/2403.08495/) (2024)
- [Towards a Client-Centered Assessment of LLM Therapists by Client Simulation](https://mlsanity.com/paper/2406.12266/) (2024)
- [RESPER: Computationally Modelling Resisting Strategies in Persuasive Conversations](https://mlsanity.com/paper/2101.10545/) (2021)
- [Speaker Verification in Agent-Generated Conversations](https://mlsanity.com/paper/2405.10150/) (2024)

## Built on by (most cited first, 14 of 14)

- [PatientHub: A Unified Framework for Patient Simulation](https://mlsanity.com/paper/2602.11684/) (2026)
- [CALM-IT: Enhancing Synthetic Motivational Interviewing Dialogue Quality with Conversational Dynamics](https://mlsanity.com/paper/2601.10085/) (2026)
- [MHSafeEval: Role-Aware Interaction-Level Evaluation of Mental Health Safety in Large Language Models](https://mlsanity.com/paper/2604.17730/) (2026)
- [Know You Before You Speak: User-State Modeling for LLM Personalization in Multi-Turn Conversation](https://mlsanity.com/paper/2605.24647/) (2026)
- [Responsible Evaluation of AI for Mental Health](https://mlsanity.com/paper/2602.00065/) (2026)
- [PsyCLIENT: Client Simulation via Conversational Trajectory Modeling for Trainee Practice and Model Evaluation in Mental Health Counseling](https://mlsanity.com/paper/2601.07312/) (2026)
- [Stress-Testing Emotional Support Models: Moving from Homogeneous to Diverse Help Seekers](https://mlsanity.com/paper/2601.07698/) (2026)
- [PatientAct: Theory-Grounded Mental Health Client Simulation](https://mlsanity.com/paper/2608.12750/) (2026)
- [Disclosure-Gated User Simulation for Companion-Agent Evaluation](https://mlsanity.com/paper/2609.00982/) (2026)
- ["Is This Really a Human Peer Supporter?": Misalignments Between Peer Supporters and Experts in LLM-Supported Interactions](https://mlsanity.com/paper/2506.09354/) (2025)
- ["I Said Things I Needed to Hear Myself": Peer Support as an Emotional, Organisational, and Sociotechnical Practice in Singapore](https://mlsanity.com/paper/2506.09362/) (2025)
- [GenAI for Social Work Field Education: Client Simulation with Real-Time Feedback](https://mlsanity.com/paper/2601.18517/) (2026)
- ["Act Like a 5th Grader" is Not Enough: Bounding Knowledge in LLM-Based User Simulators](https://mlsanity.com/paper/2608.30033/) (2026)
- [Adapting from Downturns: Prediction of Long-Term Conversational-Skill Development in Mental-Health Crisis Counselors](https://mlsanity.com/paper/2609.04350/) (2026)

## Abstract (arXiv)

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.