@inproceedings{qiu-lan-2026-interactive,
title = "Interactive Agents: Simulating Counselor-Client Psychological Counseling via Role-Playing {LLM}-to-{LLM} Interactions",
author = "Qiu, Huachuan and
Lan, Zhenzhong",
editor = "Mohammad, Saif M. and
Ousidhoum, Nedjma",
booktitle = "Proceedings of the 15th Joint Conference on Lexical and Computational Semantics (*{SEM} 2026)",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://preview.aclanthology.org/ingest-acl-workshops/2026.starsem-conference.29/",
pages = "410--427",
ISBN = "979-8-89176-413-2",
abstract = "Creating effective dialogue systems for mental health support requires high-quality multi-turn counseling dialogue data, yet collecting real counselor-client conversations presents significant challenges, including privacy concerns, high costs, and limited scalability. We present \textbf{Interactive Agents}, a novel framework that simulates naturalistic counseling dialogues through controlled LLM-to-LLM interactions. The framework introduces two key innovations: (1) a personalized client agent that maintains consistent psychological characteristics throughout a session, and (2) a counselor agent that implements a theoretically grounded three-stage therapeutic model comprising the exploration, insight, and action phases. Through rigorous evaluation using both automatic metrics and professional-counselor assessments based on the Working Alliance Inventory, we demonstrate that our framework generates therapeutically valid dialogues that are comparable in quality to human-generated sessions. Models fine-tuned on our proposed synthetic dataset (SimPsyDial) achieve state-of-the-art performance in a standard pairwise chatbot-arena evaluation of LLM-based counselors. Our framework provides a scalable, privacy-preserving method for generating high-quality counseling dialogue data while maintaining professional therapeutic standards."
}Markdown (Informal)
[Interactive Agents: Simulating Counselor-Client Psychological Counseling via Role-Playing LLM-to-LLM Interactions](https://preview.aclanthology.org/ingest-acl-workshops/2026.starsem-conference.29/) (Qiu & Lan, *SEM 2026)
ACL