The study compares counselling sessions where novice counsellors interact with human role-players versus sessions where the clients are simulated by the Mistral Mixtral 8x7b large language model. Using a sentence‑level counselling communication pattern system, the researchers examined whether LLM‑generated responses replicate authentic conversational dynamics and how counsellors’ communication behaviour changes between the two settings. Results show both similarities and differences in counselling pattern usage, suggesting that LLM‑based role‑plays can enhance counselling training while also highlighting areas needing improvement in virtual client simulations.
PsyCLIENT is a framework that uses large language models to simulate counseling clients by conditioning responses on client profiles, dialogue histories, and conversational trajectories that specify target behaviors and content constraints. The authors created PsyCLIENT-CP, a dataset of 120 Chinese client profiles covering 60 counseling topics, and evaluated the system with 24 professional counselors, finding higher authenticity and training utility ratings compared to baseline methods. In a source-identification study, PsyCLIENT-generated dialogues were more often mistaken for real human-client interactions than those from other models, indicating the effectiveness of conversational trajectory modeling in producing realistic counseling conversations.
By Huachuan Qiu, Zhaoming Chen, Yuqian Chen, Yuan Xie, Yu Lu, Zhenzhong Lan
The paper introduces StratCBT, a new dataset of 9,688 psychological counseling sessions with 256K utterances, each counselor response aligned to one of eight Cognitive Behavioral Therapy (CBT) strategies. It was created by modeling clients’ negative thoughts and generating high‑quality conversations through self‑chat, using realistic sessions for guidance. Experiments show that strategy‑aligned generation improves professional and effective counseling when evaluated with large language model‑simulated clients.
By Zimu Wang, Yiwen Jiang, Xiangyu Zhao, Yaling Shen, Jiahe Liu, Stephanie Fong, Maxmartwell H Cheng, Guilherme C Oliveira, Anh Nguyen, Robert Desimone, Barnaby Nelson, Dominic Dwyer, Zongyuan Ge
arXiv:2610.08250v1 Announce Type: new
Abstract: Large language models are increasingly used to simulate clients for counselor training and psychological counseling research, but reliable simulation r...
By Shixin Peng, Kun Jiang, Jiaxing Zheng, Qihao Yang, Jingying Chen
arXiv:2608. 07499v1 Announce Type: cross Abstract: The development and benchmarking of Large Language Model (LLM)-based Motivational Interviewing (MI) counsellors now often rely on LLM-based simulated clients.
By Jiading Zhu, Xinyu Cindy Wang, Thomas Nguyen, Yan Qing Lee, Osnat C. Melamed, Peter Selby, Jonathan Rose
arXiv:2409.02244v3 Announce Type: replace-cross
Abstract: Large language models (LLMs) are increasingly being used as ad hoc therapists. While prior research has found that LLMs outperform human coun...
By Zainab Iftikhar, Sean Ransom, Amy Xiao, Nicole Nugent, Jeff Huang
arXiv:2608.22251v1 Announce Type: cross
Abstract: Rising global demand for mental health support creates significant service delivery challenges, with asynchronous email counselling serving as a cruc...
By Philipp Steigerwald, Nico Bienlein, Jennifer Burghardt, Mara Stieler, Robert Lehmann, Jens Albrecht
The paper introduces a three-tier persona vector for user simulation in evaluating LLM agents, comprising 23 dimensions across demographics, behavioral traits, and emotional states, plus a query-complexity overlay. It demonstrates that these nuanced personas generate diverse, scenario-reactive conversations, leading to significant variations in agent goal achievement and compliance across different contexts. The model’s design allows for reproducible, auditable user behavior patterns without relying on learned covariance matrices.
By Rahul Khedar, Eshita, Sneha Teja Sree Reddy Thondapu, Mayank Malhotra, Arup Kumar Das, Jitesh Chandra Mishra, Arun Menon, Avinash Karn, Mouli V
arXiv:2608. 12750v1 Announce Type: cross Abstract: LLM-based simulated clients are increasingly used to train novice counselors, evaluate LLM therapists, and generate synthetic data.
By Sahand Sabour, TszYam NG, Yaqian Chen, Guanqun Bi, Jialu Zhao, Minlie Huang
The paper introduces a three-tier persona vector to generate diverse, realistic user inputs for evaluating tool-augmented LLM agents. The vector includes 23 dimensions: categorical demographics, continuous behavioral traits, and continuous emotional states, plus a query-complexity overlay. Experiments on 64,698 conversations show that these persona dimensions produce measurable differences in agent performance and realistic scenario-reactive behavior.
arXiv:2608.22731v1 Announce Type: new
Abstract: Nonverbal behavior generation systems for virtual agents often take an utterance as input and generate nonverbal behaviors that emphasize or illustrate...
By Parisa Ghanad Torshizi, Stacy Marsella
Graph2Counsel is a framework that generates synthetic counseling dialogues by leveraging Client Psychological Graphs (CPGs) to encode the relationships among a client’s thoughts, emotions, and behaviors. The system uses a structured prompting pipeline guided by counselor strategies and explores techniques such as Chain‑of‑Thought and Multi‑Agent Feedback to produce 760 realistic sessions from 76 CPGs. Expert evaluation shows the dataset surpasses previous ones in specificity, counselor competence, authenticity, conversational flow, and safety, and fine‑tuning an open‑source model on it improves performance on several counseling benchmarks.
By Aishik Mandal, Hiba Arnaout, Clarissa W. Ong, Juliet Bockhorst, Kate Sheehan, Rachael Moldow, Tanmoy Chakraborty, Iryna Gurevych