LLM的情感光谱：利用共情与基于情感的标记物进行心理健康支持
The Emotional Spectrum of LLMs: Leveraging Empathy and Emotion-Based Markers for Mental Health Support
AI总结：
本文提出对话系统RACLETTE，通过可解释情感画像与共情对话模型提升心理健康支持中的情感理解与回应能力，并将情感画像作为可解释标记物用于精神障碍的初步筛查。
摘要：
对心理健康服务日益增长的需求凸显了创新解决方案的必要性，尤其是在心理对话式人工智能领域，该领域中敏感数据的可用性十分稀缺。在这项工作中，我们探索了开发一个专为心理健康支持而设计的系统，该系统采用一种新颖的心理评估方法，基于可解释的情感画像并结合共情对话模型，为增强传统护理提供了一种有前景的工具，尤其是在无法立即获得专业知识的场景中。我们的工作可分为两个主要部分，二者内在紧密相连。首先，我们提出了RACLETTE，这是一个对话系统，在理解用户情绪状态和在对话中生成共情回应方面，与最先进的基准相比均展现出更优的情感准确性，同时通过用户的交互逐步构建其情感画像。其次，我们展示了用户的情感画像如何被用作心理健康评估的可解释标记物。这些画像可以与不同精神障碍相关的特征性情感模式进行比较，从而为初步筛查和支持提供一种新颖的方法。
英文摘要：
The increasing demand for mental health services has highlighted the need for innovative solutions, particularly in the realm of psychological conversational AI, where the availability of sensitive data is scarce. In this work, we explored the development of a system tailored for mental health support with a novel approach to psychological assessment based on explainable emotional profiles in combination with empathetic conversational models, offering a promising tool for augmenting traditional care, particularly where immediate expertise is unavailable. Our work can be divided into two main parts, intrinsecaly connected to each other. First, we present RACLETTE, a conversational system that demonstrates superior emotional accuracy compared to state-of-the-art benchmarks in both understanding users' emotional states and generating empathetic responses during conversations, while progressively building an emotional profile of the user through their interactions. Second, we show how the emotional profiles of a user can be used as interpretable markers for mental health assessment. These profiles can be compared with characteristic emotional patterns associated with different mental disorders, providing a novel approach to preliminary screening and support.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
