The Emotional Spectrum of LLMs: Leveraging Empathy and Emotion-Based Markers for Mental Health Support
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.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Heartificial Intelligence: Exploring Empathy in Language Models
Large language models have become increasingly common, used by millions of people worldwide in both professional and personal contexts. As these models continue to advance, they are frequently serving as virtual assistan…
From Generation to Collaboration: Using LLMs to Edit for Empathy in Healthcare
Clinical empathy is essential for patient care, but physicians need continually balance emotional warmth with factual precision under the cognitive and emotional constraints of clinical practice. This study investigates …
APTNESS: Incorporating Appraisal Theory and Emotion Support Strategies for Empathetic Response Generation
Empathetic response generation is designed to comprehend the emotions of others and select the most appropriate strategies to assist them in resolving emotional challenges. Empathy can be categorized into cognitive empat…
Empathetic Response GenerationResponse GenerationRetrievalSemantic RetrievalE3RG: Building Explicit Emotion-driven Empathetic Response Generation System with Multimodal Large Language Model
Multimodal Empathetic Response Generation (MERG) is crucial for building emotionally intelligent human-computer interactions. Although large language models (LLMs) have improved text-based ERG, challenges remain in handl…
Empathetic Response GenerationFrom Generic Empathy to Personalized Emotional Support: A Self-Evolution Framework for User Preference Alignment
Effective emotional support hinges on understanding users' emotions and needs to provide meaningful comfort during multi-turn interactions. Large Language Models (LLMs) show great potential for expressing empathy; howeve…