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.

本研究解决了心理健康服务需求增加但缺乏敏感数据的问题，提出了一种新颖的心理评估系统，结合可解释的情感档案和同理心对话模型，为传统护理提供了一种有前景的补充工具。研究表明，该系统在理解用户情感状态和生成同理心回应方面表现出色，并利用用户的情感档案作为心理健康评估的可解释标记，促进了初步筛查和支持的创新方法。

大型语言模型的情感谱系：利用同理心和情感标记进行心理健康支持