@inproceedings{zhou-etal-2025-crisp,
title = "Crisp: Cognitive Restructuring of Negative Thoughts through Multi-turn Supportive Dialogues",
author = "Zhou, Jinfeng and
Chen, Yuxuan and
Yin, Jianing and
Huang, Yongkang and
Shi, Yihan and
Zhang, Xikun and
Peng, Libiao and
Zhang, Rongsheng and
Lv, Tangjie and
Hu, Zhipeng and
Wang, Hongning and
Huang, Minlie",
editor = "Christodoulopoulos, Christos and
Chakraborty, Tanmoy and
Rose, Carolyn and
Peng, Violet",
booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.emnlp-main.1652/",
pages = "32462--32491",
ISBN = "979-8-89176-332-6",
abstract = "Cognitive Restructuring (CR) uses multi-turn dialogue to identify and restructure one{'}s negative thoughts, arising from mental health issues, into more helpful and positive ones. Clinician shortage and stigma urge the development of human-LLM interactive psychotherapy for CR. Yet, effectively implementing CR is hindered by entrenched cognitive distortions, emotional resistance, and individual differences, which existing works have not overcome. To bridge this gap, we propose CRDial, a novel framework that structures CR as theory-grounded multi-stage multi-turn dialogue, integrating multi-aspect supportive strategies for emotional management and a multi-channel loop mechanism to account for diverse individual distortions. 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."
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<abstract>Cognitive Restructuring (CR) uses multi-turn dialogue to identify and restructure one’s negative thoughts, arising from mental health issues, into more helpful and positive ones. Clinician shortage and stigma urge the development of human-LLM interactive psychotherapy for CR. Yet, effectively implementing CR is hindered by entrenched cognitive distortions, emotional resistance, and individual differences, which existing works have not overcome. To bridge this gap, we propose CRDial, a novel framework that structures CR as theory-grounded multi-stage multi-turn dialogue, integrating multi-aspect supportive strategies for emotional management and a multi-channel loop mechanism to account for diverse individual distortions. 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.</abstract>
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%0 Conference Proceedings
%T Crisp: Cognitive Restructuring of Negative Thoughts through Multi-turn Supportive Dialogues
%A Zhou, Jinfeng
%A Chen, Yuxuan
%A Yin, Jianing
%A Huang, Yongkang
%A Shi, Yihan
%A Zhang, Xikun
%A Peng, Libiao
%A Zhang, Rongsheng
%A Lv, Tangjie
%A Hu, Zhipeng
%A Wang, Hongning
%A Huang, Minlie
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-332-6
%F zhou-etal-2025-crisp
%X Cognitive Restructuring (CR) uses multi-turn dialogue to identify and restructure one’s negative thoughts, arising from mental health issues, into more helpful and positive ones. Clinician shortage and stigma urge the development of human-LLM interactive psychotherapy for CR. Yet, effectively implementing CR is hindered by entrenched cognitive distortions, emotional resistance, and individual differences, which existing works have not overcome. To bridge this gap, we propose CRDial, a novel framework that structures CR as theory-grounded multi-stage multi-turn dialogue, integrating multi-aspect supportive strategies for emotional management and a multi-channel loop mechanism to account for diverse individual distortions. 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.
%U https://aclanthology.org/2025.emnlp-main.1652/
%P 32462-32491
Markdown (Informal)
[Crisp: Cognitive Restructuring of Negative Thoughts through Multi-turn Supportive Dialogues](https://aclanthology.org/2025.emnlp-main.1652/) (Zhou et al., EMNLP 2025)
ACL
- Jinfeng Zhou, Yuxuan Chen, Jianing Yin, Yongkang Huang, Yihan Shi, Xikun Zhang, Libiao Peng, Rongsheng Zhang, Tangjie Lv, Zhipeng Hu, Hongning Wang, and Minlie Huang. 2025. Crisp: Cognitive Restructuring of Negative Thoughts through Multi-turn Supportive Dialogues. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pages 32462–32491, Suzhou, China. Association for Computational Linguistics.