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
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
The paper examines how Large Language Models (LLMs) can act as simulated clients in online counselling training, comparing their performance to human role-players. Researchers recorded sessions where novice counsellors interacted with both human peers and LLM-generated clients (using Mistral’s Mixtral 8x7b with 4‑bit quantization), then analyzed the interactions at the sentence level using a counselling communication pattern system. Results show both similarities and differences in counselling patterns between the two scenarios, suggesting that LLM-based role‑plays can support competency development while also highlighting areas needing improvement in virtual client simulations.
By Eric Rudolph, Philipp Steigerwald, Jens Albrecht
The paper introduces PsyLLM, a large language model specifically designed for mental health counseling that integrates diagnostic reasoning and multiple therapeutic frameworks. It employs an automated data synthesis pipeline that transforms Reddit mental‑health posts into multi‑turn counseling dialogues, guided by DSM/ICD standards and therapies such as CBT, ACT, and psychodynamic approaches. A new benchmark and evaluation protocol show that PsyLLM outperforms baseline models, with results validated by professional counseling experts.
By He Hu, Juzheng Si, Qianning Wang, Fei Ma, Fuji Ren, Sicheng Zhao, Yucheng Zhou, Laizhong Cui
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:2605.22140v3 Announce Type: replace
Abstract: Psychological counseling unfolds over many sessions, and what a client brings to each session depends on what has happened in their life since the...
By Chaogui Gou
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:2510.25384v2 Announce Type: replace
Abstract: Large Language Models (LLMs) are promising tools for synthetic data generation in mental health. However, privacy policies and restrictions forced...
By Doan Nam Long Vu, Rui Tan, Lena Moench, Svenja Jule Francke, Daniel Woiwod, Florian Thomas-Odenthal, Sanna Stroth, Tilo Kircher, Christiane Hermann, Udo Dannlowski, Hamidreza Jamalabadi, Simone Balloccu, Shaoxiong Ji
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.
arXiv:2608.22615v1 Announce Type: new
Abstract: Large Language Model (LLM)-based counseling agents can generate fluent and supportive responses, but they often lack the structured, goal-directed prog...
By Qi Zhang, Heajun An, Prakriti Dumaru, Sang Won Lee, Lifu Huang, Pamela J. Wisniewski, Jin-Hee Cho
The paper investigates the role of minimal responses—short, empathic utterances—in psychological counseling, noting that such brief replies are common in human dialogues but underrepresented in large language model (LLM) outputs. Using a two‑stage filtering approach and contextual verification with an LLM, the authors systematically analyze minimal responses across multiple counseling datasets. They find that while strong commercial LLMs can produce minimal replies when prompted, they often fail to judge when these replies are appropriate, and counseling‑specific models trained on synthetic data tend to generate longer, content‑rich responses instead.
By Zhiyang Qi
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