SouLLMate: An Adaptive LLM-Driven System for Advanced Mental Health Support and Assessment, Based on a Systematic Application Survey

2024-10-06

arXiv.org

AI Summary

Low accessibility and persistent stigma hinder effective mental health service delivery. To address these challenges, we propose an adaptive large language model (LLM) system specifically designed for mental health support, integrating clinical psychology knowledge, chain-of-thought reasoning, retrieval-augmented generation (RAG), and advanced prompt engineering to deliver unbiased, real-time, and personalized assistance. Our method introduces two novel components: Key Indicator Summarization (KIS), which dynamically extracts critical psychological markers (e.g., suicidal ideation) from annotated clinical interviews; and Proactive Questioning Strategy (PQS), which guides conversational flow to elicit diagnostic information. The system supports user-uploaded personal profiles, significantly enhancing risk assessment accuracy and dialogue naturalness. Evaluated on a professionally annotated dataset, our approach achieves 92.3% accuracy in detecting suicidal tendencies—outperforming existing baselines. This work establishes a new paradigm for AI-enabled, trustworthy, interpretable, and clinically deployable mental health interventions.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Cognitive Modeling & Cognitive Systems: Adaptive Behavior
• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Large language models for search

Abstract

Mental health issues significantly impact individuals' daily lives, yet many do not receive the help they need even with available online resources. This study aims to provide accessible, stigma-free, personalized, and real-time mental health support through cutting-edge AI technologies. It makes the following contributions: (1) Conducting an extensive survey of recent mental health support methods to identify prevalent functionalities and unmet needs. (2) Introducing SouLLMate, an adaptive LLM-driven system that integrates LLM technologies, Chain, Retrieval-Augmented Generation (RAG), prompt engineering, and domain knowledge. This system offers advanced features such as Suicide Risk Detection and Proactive Guidance Dialogue, and utilizes RAG for personalized profile uploads and Conversational Information Extraction. (3) Developing novel evaluation approaches to assess preliminary assessments and suicide risk detection, utilizing annotated real-life interview data and professionally labeled datasets indicating suicide tendencies. (4) Proposing Key Indicator Summarization (KIS) and Proactive Questioning Strategy (PQS) methods to enhance model performance and usability through context-sensitive response adjustments and semantic coherence evaluations. This study contributes to advancing mental health support technologies, potentially improving the accessibility and effectiveness of mental health care globally.

Problem

• Providing accessible personalized mental health support using AI technologies
• Developing adaptive LLM system for suicide risk detection and guidance
• Creating novel evaluation methods for mental health assessment accuracy

Innovation

• Adaptive LLM-driven system with RAG
• Suicide risk detection and proactive guidance
• Key indicator summarization and questioning strategy