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Prompt Engineering an Informational Chatbot for Education on Mental Health Using a Multiagent Approach for Enhanced

Per Niklas Waaler1, Musarrat Hussain1, Igor Molchanov1

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Summary
This summary is machine-generated.

A critical analysis filter (CAF) improved large language model (LLM) chatbot compliance in mental health education. This system ensures chatbots provide accurate schizophrenia information, enhancing safety and reliability.

Keywords:
AI in health careAI safetyAI transparencyGPT-4LLMadaptive learninglarge language modellimiting scope of AImental healthprompt engineeringschizophreniaself-reflection

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Area of Science:

  • Artificial Intelligence in Mental Health
  • Natural Language Processing Applications
  • Schizophrenia Patient Education

Background:

  • Cognitive impairments in schizophrenia impact patient education.
  • Large language models (LLMs) offer accessible mental health information.
  • LLM controllability and hallucination risks necessitate safety measures.

Purpose of the Study:

  • Develop and evaluate a critical analysis filter (CAF) system.
  • Ensure LLM chatbots adhere to instructions and scope.
  • Deliver validated mental health information for schizophrenia patients.

Main Methods:

  • Prompt-engineered GPT-4 chatbot for schizophrenia education.
  • CAF system using LLM agents for response analysis and refinement.
  • Adversarial conversations to test CAF's ability to prevent role drift and hallucinations.
  • Human rating of chatbot responses for integrity and adherence to source material.

Main Results:

  • CAF activation improved response compliance significantly.
  • 81% of responses were compliant with CAF enabled, versus 8.3% when disabled.
  • CAF demonstrated effectiveness in maintaining chatbot integrity and accuracy.

Conclusions:

  • Self-reflection mechanisms in LLMs can enable safe and effective mental health platforms.
  • This approach balances LLM flexibility with reliable scope control.
  • Further testing in realistic scenarios is recommended for widespread adoption.