Roleplaying with Structure: Synthetic Therapist-Client Conversation Generation from Questionnaires
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Computer Science > Computation and Language
arXiv:2510.25384 (cs)
[Submitted on 29 Oct 2025 (v1), last revised 28 Aug 2026 (this version, v2)]
Title:Roleplaying with Structure: Synthetic Therapist-Client Conversation Generation from Questionnaires
Authors: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
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Abstract:Large Language Models (LLMs) are promising tools for synthetic data generation in mental health. However, privacy policies and restrictions forced previous work to rely mainly on generic information. We present a comprehensive corpus of synthetic therapist-client conversations generated through LLMs. We construct our generation pipeline, SQPsych (Structured Questionnaire-based Psychotherapy), which uses real structured client profiles and psychological questionnaires without leaking any sensitive data. We fine-tune various open-weight LLMs on our generated corpus, SQPsychConv , and test them through both automatic benchmarks and human evaluation with trained psychotherapists. We find that standard benchmarks do not adequately capture the strengths of our dataset, but expert judgment shows that SQPsych makes LLMs significantly better at therapist roleplaying. Experts also consistently prefer therapy sessions generated by our models compared to other mental-health-oriented LLMs. We release our code, fine-tuned models SQPsychLLM, and corpora at this https URL.
| Comments: | Accepted to the 5th Workshop on NLP for Positive Impact 2026 @EMNLP 2026, Budapest, Hungary |
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2510.25384 [cs.CL] |
| (or arXiv:2510.25384v2 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2510.25384
arXiv-issued DOI via DataCite
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Submission history
From: Doan Nam Long Vu [view email][v1] Wed, 29 Oct 2025 10:55:52 UTC (1,293 KB)
[v2] Fri, 28 Aug 2026 13:42:28 UTC (838 KB)
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