Summary of Prompt Engineering a Schizophrenia Chatbot: Utilizing a Multi-agent Approach For Enhanced Compliance with Prompt Instructions, by Per Niklas Waaler et al.
Prompt Engineering a Schizophrenia Chatbot: Utilizing a Multi-Agent Approach for Enhanced Compliance with Prompt Instructions
by Per Niklas Waaler, Musarrat Hussain, Igor Molchanov, Lars Ailo Bongo, Brita Elvevåg
First submitted to arxiv on: 10 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The abstract presents a novel approach for building an educational platform for individuals with schizophrenia. Large Language Models (LLMs) like GPT-4 can provide accessible and engaging information on mental health topics, but concerns about ethics and safety arise due to their black-box nature. To address these issues, the authors propose a Critical Analysis Filter that utilizes prompted LLM agents to critically analyze and refine chatbot responses, providing real-time feedback. This approach is tested by developing an informational schizophrenia chatbot and comparing its performance with and without the filter. The results show that activating the filter improves compliance scores (>=2) in 67.0% of responses, compared to only 8.7% when deactivated. The authors suggest that this self-reflection layer can enable LLMs for safe and effective use in mental health platforms. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper talks about building a special kind of educational platform for people with schizophrenia. They want to make sure the information is accurate and easy to understand, but also worry about whether it’s safe and ethical. To solve this problem, they came up with an idea called the Critical Analysis Filter. It uses special computer programs to review what the chatbot says and make sure it stays on topic. The authors tested this idea by building a chatbot that talks about schizophrenia and found that when they used the filter, the chatbot was much more likely to give accurate answers. |
Keywords
» Artificial intelligence » Gpt
