Interactive Agents: Simulating Counselor-Client Psychological Counseling via Role-Playing LLM-to-LLM Interactions

Creating effective dialogue systems for mental health support requires high-quality multiturn counseling dialogue data, yet collecting real counselor-client conversations presents significant challenges, including privacy concerns, high costs, and limited scalability.We present 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 stateof-the-art performance in a standard pairwise chatbot-arena evaluation of LLM-based counselors.Our framework provides a scalable, privacy-preserving method for generating highquality counseling dialogue data while maintaining professional therapeutic standards.1 * Corresponding author. 1 The code, data, and models are available at https://github.com/qiuhuachuan/interactive-agents.Simulating human-to-human conversational qa via zero-shot llm-to-llm interactions.