CFlowPsyD: An Analysis-Enhanced Dataset for Asynchronous Psychological Counseling through Self-Optimizing Multi-Agent Framework

ACL ARR 2026 January Submission3699 Authors

Keywords: Asynchronous Psychological Counseling, Analysis-Enhanced, Multi-Agent

Abstract: Asynchronous psychological counseling (APC) represents a crucial mental health service modality that transcends temporal and spatial constraints. However, its development faces significant data scarcity challenges: due to stringent privacy protection requirements and professional ethical considerations, direct collection of conversational data from authentic APC scenarios is virtually impossible. To address this challenge, we design a self-optimizing multi-agent framework for counseling dialogue generation, \textbf{CFlowPsy}, which utilizes a small amount of real anonymized counseling cases as seed data to synthesize diverse problem-solving-oriented APC conversations through large language models. Specifically, the framework employs a Persona-Flow module to continuously track and update clients' basic information, real-time emotions, and counseling progress, providing dynamic personalized analytical support for counselors and enabling self-optimization of generated dialogues. Simultaneously, the framework ensures that generated interventions contain explicit reasoning processes, demonstrating clear psychological analysis and logic, thereby enhancing the accuracy and consistency of responses. Under this framework, we develop the first Chinese APC dataset, \textbf{CFlowPsyD}, comprising 1,700 high-quality extended conversations. Extensive experiments and human evaluations confirm that the proposed CFlowPsyD dataset successfully simulates human-like APC processes.

Paper Type: Long

Research Area: Dialogue and Interactive Systems

Research Area Keywords: dialogue state tracking, conversational modeling

Contribution Types: NLP engineering experiment, Data resources

Languages Studied: Chinese

Submission Number: 3699