用心理急救框架构建恐慌干预模型，实测效果优于主流方法。
PanicToCalm: A Proactive Counseling Agent for Panic Attacks
- 基于第一人称叙事构建高压力数据集，支持心理急救原则
- 模型在模拟和真人评估中均显著改善求助者情绪状态
- 适合心理健康助手开发、临床辅助系统设计人员参考
恐慌发作是急性恐惧与痛苦的发作，及时、恰当的干预可显著帮助个体恢复稳定。然而，由于伦理与物流问题，适用于训练此类模型的数据集仍十分稀缺。为此，我们提出PACE数据集，该数据集基于第一人称叙述构建高压力情境，并遵循心理急救（PFA）原则。基于此数据，我们训练了PACER模型，该模型旨在提供共情与指导双重支持，通过监督学习与模拟偏好对齐进行优化。为评估其有效性，我们提出了多维度的PanicEval评估框架，涵盖一般咨询质量与危机特异性策略。实验结果表明，PACER在咨询师侧指标与来访者情绪改善方面均优于强基线模型。人类评估进一步验证其实际价值，在恐慌场景中始终优于通用模型、认知行为疗法（CBT）模型及GPT-4驱动模型（代码已公开于https://github.com/JihyunLee1/PanicToCalm）。
原文摘要 · Abstract (English)
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 (Code is available at https://github.com/JihyunLee1/PanicToCalm ).
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