Steering the Compass: Aligning Dynamic Psychological Counseling Conversations with Cognitive Behavioral Therapy Strategies

2026-09-17

AI Summary

本文通过构建包含9,688个会话的StratCBT数据集，旨在解决心理辅导对话中缺乏动态决策的问题，提升认知行为疗法的效果。

Abstract

Recent advancements in large language models have revolutionized the field of psychological counseling, especially in the context of Cognitive Behavioral Therapy (CBT). While the success of CBT relies heavily on dynamic decision-making informed by the client's real-time mental state, this aspect has often been overlooked in current research, limiting both flexibility and therapeutic outcomes. In this paper, we introduce StratCBT, a dataset specifically designed for psychological counseling conversations with CBT Strategies, consisting of 9,688 sessions and around 256K utterances, with each counselor's response aligned with one of eight distinct strategies. The creation of StratCBT involves modeling clients based on their negative thoughts and generating high-quality counseling conversations through self-chat, incorporating realistic sessions as guidance, thereby significantly surpassing existing datasets in both general counseling and CBT-specific skills. We conduct extensive experiments to demonstrate the effectiveness of strategy-aligned generation and evaluate its efficacy in delivering professional and effective counseling with LLM-simulated clients to reflect real-world scenarios. The dataset can be obtained from https://github.com/zimuwangnlp/StratCBT.

Problem

• Cognitive Behavioral Therapy
• Dynamic Decision-making
• Real-time Mental State
• Therapeutic Outcomes
• Psychological Counseling

Innovation

• StratCBT
• Cognitive Behavioral Therapy (CBT)
• strategy-aligned generation
• psychological counseling conversations
• large language models