@inproceedings{lee-etal-2025-panictocalm,
title = "{P}anic{T}o{C}alm: A Proactive Counseling Agent for Panic Attacks",
author = "Lee, Jihyun and
Min, Yejin and
Kim, San and
Jeon, Yejin and
Yang, Sung Jun and
Kim, Hyounghun and
Lee, Gary",
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.649/",
doi = "10.18653/v1/2025.emnlp-main.649",
pages = "12842--12874",
ISBN = "979-8-89176-332-6",
abstract = "Panic attacks are acute episodes of fear and distress, in which timely, appropriate intervention can significantly help individuals regain stability. However, suitable datasets for training such models remain scarce due to ethical and logistical issues. To address this, we introduce Pace, which is a dataset that includes high-distress episodes constructed from first-person narratives, and structured around the principles of Psychological First Aid (PFA). Using this data, we train Pacer, a counseling model designed to provide both empathetic and directive support, which is optimized through supervised learning and simulated preference alignment. To assess its effectiveness, we propose PanicEval, a multi-dimensional framework covering general counseling quality and crisis-specific strategies. Experimental results show that Pacer outperforms strong baselines in both counselor-side metrics and client affect improvement. Human evaluations further confirm its practical value, with Pacer consistently preferred over general, CBT-based, and GPT-4-powered models in panic scenarios."
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<abstract>Panic attacks are acute episodes of fear and distress, in which timely, appropriate intervention can significantly help individuals regain stability. However, suitable datasets for training such models remain scarce due to ethical and logistical issues. To address this, we introduce Pace, which is a dataset that includes high-distress episodes constructed from first-person narratives, and structured around the principles of Psychological First Aid (PFA). Using this data, we train Pacer, a counseling model designed to provide both empathetic and directive support, which is optimized through supervised learning and simulated preference alignment. To assess its effectiveness, we propose PanicEval, a multi-dimensional framework covering general counseling quality and crisis-specific strategies. Experimental results show that Pacer outperforms strong baselines in both counselor-side metrics and client affect improvement. Human evaluations further confirm its practical value, with Pacer consistently preferred over general, CBT-based, and GPT-4-powered models in panic scenarios.</abstract>
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%0 Conference Proceedings
%T PanicToCalm: A Proactive Counseling Agent for Panic Attacks
%A Lee, Jihyun
%A Min, Yejin
%A Kim, San
%A Jeon, Yejin
%A Yang, Sung Jun
%A Kim, Hyounghun
%A Lee, Gary
%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 lee-etal-2025-panictocalm
%X Panic attacks are acute episodes of fear and distress, in which timely, appropriate intervention can significantly help individuals regain stability. However, suitable datasets for training such models remain scarce due to ethical and logistical issues. To address this, we introduce Pace, which is a dataset that includes high-distress episodes constructed from first-person narratives, and structured around the principles of Psychological First Aid (PFA). Using this data, we train Pacer, a counseling model designed to provide both empathetic and directive support, which is optimized through supervised learning and simulated preference alignment. To assess its effectiveness, we propose PanicEval, a multi-dimensional framework covering general counseling quality and crisis-specific strategies. Experimental results show that Pacer outperforms strong baselines in both counselor-side metrics and client affect improvement. Human evaluations further confirm its practical value, with Pacer consistently preferred over general, CBT-based, and GPT-4-powered models in panic scenarios.
%R 10.18653/v1/2025.emnlp-main.649
%U https://aclanthology.org/2025.emnlp-main.649/
%U https://doi.org/10.18653/v1/2025.emnlp-main.649
%P 12842-12874
Markdown (Informal)
[PanicToCalm: A Proactive Counseling Agent for Panic Attacks](https://aclanthology.org/2025.emnlp-main.649/) (Lee et al., EMNLP 2025)
ACL
- Jihyun Lee, Yejin Min, San Kim, Yejin Jeon, Sung Jun Yang, Hyounghun Kim, and Gary Lee. 2025. PanicToCalm: A Proactive Counseling Agent for Panic Attacks. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pages 12842–12874, Suzhou, China. Association for Computational Linguistics.