Three-tier LLM system mimics psychologist questioning patterns while maintaining privacy through local deployment
📚 https://arxiv.org/abs/2410.16322
Original Problem 🎯:
Mental health support remains inaccessible to many due to cost, stigma, and resource limitations. Current AI solutions lack personalization, proactive engagement, and reliable risk detection capabilities.
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Solution in this Paper 🛠️:
• SouLLMate: A three-level LLM system combining Chain, RAG, and prompt engineering
• Key Indicator Summarization (KIS): Extracts critical information from historical dialogues
• Proactive Questioning Strategy (PQS): Mimics psychologist's assessment approach
• Stacked Multi-Model Reasoning (SMMR): Enhances long-context reasoning accuracy
• System integrates RAG for personalized profile management and information extraction
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Key Insights 💡:
• Mental health support needs personalization and proactive engagement
• Multi-level LLM architecture improves assessment accuracy
• Combining domain expertise with AI enhances mental health support
• Local deployment ensures data privacy and security
• System serves both professionals and help seekers effectively
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Results 📊:
• 80% accuracy in clinical mental health assessments under zero-shot conditions
• Enhanced performance through SMMR and KIS methods
• Supports 59+ languages for diverse populations
• Validated using expert-annotated mental health data
• Demonstrated strong capabilities in understanding mental health issues
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