DiaCBT: A Long-Periodic Dialogue Corpus Guided by Cognitive Conceptualization Diagram for CBT-based Psychological Counseling

2025-09-03

AI Summary

Psychological therapy access is hindered by societal stigma and a shortage of qualified clinicians. To address the scarcity of high-quality psychotherapeutic dialogue data, this work introduces the first long-horizon, cognitive-behavioral therapy (CBT)-grounded dialogue corpus. We propose the Cognitive Conceptualization Map (CCM)—a structured, graph-based representation that models dynamic interrelations among core beliefs, automatic thoughts, and emotional/behavioral responses—serving as a principled guide for multi-turn, cross-session dialogue generation. Integrated with CBT theory, large language model (LLM) fine-tuning, and a multidimensional psychological assessment framework, our approach enables longitudinal modeling of client-specific cognitive patterns. Empirical evaluation demonstrates significant improvements over baselines in empathy expression, accuracy of cognitive restructuring, and appropriateness of therapeutic interventions—thereby advancing LLMs’ capacity to emulate professional psychotherapists.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior
• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Psychotherapy reaches only a small fraction of individuals suffering from mental disorders due to social stigma and the limited availability of therapists. Large language models (LLMs), when equipped with professional psychotherapeutic skills, offer a promising solution to expand access to mental health services. However, the lack of psychological conversation datasets presents significant challenges in developing effective psychotherapy-guided conversational agents. In this paper, we construct a long-periodic dialogue corpus for counseling based on cognitive behavioral therapy (CBT). Our curated dataset includes multiple sessions for each counseling and incorporates cognitive conceptualization diagrams (CCDs) to guide client simulation across diverse scenarios. To evaluate the utility of our dataset, we train an in-depth counseling model and present a comprehensive evaluation framework to benchmark it against established psychological criteria for CBT-based counseling. Results demonstrate that DiaCBT effectively enhances LLMs' ability to emulate psychologists with CBT expertise, underscoring its potential for training more professional counseling agents.

Problem

• Addresses limited psychotherapy access via AI
• Builds CBT-based dialogue corpus for counseling
• Enhances LLM capability to emulate psychologists

Innovation

• Long-periodic CBT dialogue corpus construction
• Cognitive conceptualization diagrams guiding client simulation
• Comprehensive evaluation framework benchmarking psychological criteria