Crisp: Cognitive Restructuring of Negative Thoughts through Multi-turn Supportive Dialogues

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.

@article{zhou2025_2504.17238,
  title={ Crisp: Cognitive Restructuring of Negative Thoughts through Multi-turn Supportive Dialogues },
  author={ Jinfeng Zhou and Yuxuan Chen and Jianing Yin and Yongkang Huang and Yihan Shi and Xikun Zhang and Libiao Peng and Rongsheng Zhang and Tangjie Lv and Zhipeng Hu and Hongning Wang and Minlie Huang },
  journal={arXiv preprint arXiv:2504.17238},
  year={ 2025 }
}