用多轮对话实现心理认知重构，提升负面思维转化效果
Crisp: Cognitive Restructuring of Negative Thoughts through Multi-turn Supportive Dialogues
- 设计识别与重构双阶段对话流程，支持迭代式认知调整
- 在人类评估中，7B/14B模型在单点、成对和干预测试中均表现更优
- 适用于心理健康辅助系统，尤其适合资源短缺场景
认知重构（CR）是一种通过多轮对话识别并重塑个体负面思维的心理治疗方法，以应对心理健康挑战。由于临床医生短缺和社会偏见，亟需发展人-大模型交互式心理治疗。现有方法多采用简单文本重写、固定模式对话或单次完成的重构流程，难以契合真实心理治疗过程。为此，本文提出CRDial框架，包含专门设计的负向思维识别与重构阶段，融合句级支持性对话策略，并引入多通道循环机制实现迭代式认知重构。基于该框架，我们从大模型中提炼出Crisp这一大规模高质量双语对话数据集，并训练了7B和14B规模的基于Crisp的对话型大模型（Crispers）。大量人工研究表明，Crispers在点对点、成对及干预评估中均表现出显著优势。
原文摘要 · Abstract (English)
Cognitive Restructuring (CR) is a psychotherapeutic process aimed at identifying and restructuring an individual's negative thoughts, arising from mental health challenges, into more helpful and positive ones via multi-turn dialogues. Clinician shortage and stigma urge the development of human-LLM interactive psychotherapy for CR. Yet, existing efforts implement CR via simple text rewriting, fixed-pattern dialogues, or a one-shot CR workflow, failing to align with the psychotherapeutic process for effective CR. To address this gap, we propose CRDial, a novel framework for CR, which creates multi-turn dialogues with specifically designed identification and restructuring stages of negative thoughts, integrates sentence-level supportive conversation strategies, and adopts a multi-channel loop mechanism to enable iterative CR. With CRDial, we distill Crisp, a large-scale and high-quality bilingual dialogue dataset, from LLM. We then train Crispers, Crisp-based conversational LLMs for CR, at 7B and 14B scales. Extensive human studies show the superiority of Crispers in pointwise, pairwise, and intervention evaluations.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。