A Comprehensive Investigation of Empathetic Dialogue Systems for Mental Health Support Using Large Language Models
TL;DR
A multimodal emotion-aware architecture, which pays attention to memory-enhanced personalization and emotion-specific reinforcement learning, is introduced and hybrid human-AI approaches, which focus on safety and empathetic conversation to improve current mental health systems are recommended.
Abstract
Mental health issues are a crisis for the world, with one out of eight individuals in low-income countries with a disorder experiencing treatment gaps. This paper presents a detailed overview of how Large Language Models (LLMs) and the Transformer architecture can address these problems. The development of conversational agents as rule-based systems to advanced models, which apply Cognitive-behavioral Therapy (CBT) in minimizing anxiety and depression, is examined. The review also looks at the use of LLMs in clinical screening such as multimodal depression and suicide risk. However, existing systems have enormous challenges despite the possibility of mental health assistance, such as “feigned empathy,” hallucinations, and relying on unimodal inputs using texts. To address these limitations that exist, a multimodal emotion-aware architecture, which pays attention to memory-enhanced personalization and emotion-specific reinforcement learning, is introduced. Finally, this review recommends hybrid human-AI approaches, which focus on safety and empathetic conversation to improve current mental health systems.