How can LLMs explain their decisions in multi-agent systems?
Explaining Explaining
September 27, 2024
https://arxiv.org/pdf/2409.18052This research paper argues that while machine learning is powerful, it's difficult to explain how ML-based AI systems reach their conclusions. This is a problem for critical applications where trust and understanding are needed. The paper proposes a "hybrid" approach using LEIAs (Language-Endowed Intelligent Agents) that combine symbolic AI with machine learning.
Key points for LLM-based multi-agent systems:
- Explainability is crucial: The paper stresses that for multi-agent systems in critical applications (like healthcare), being able to understand the system's reasoning is essential, which is a challenge for pure ML approaches.
- Hybrid approach: Combining LLMs (for tasks where explainability is less crucial) with symbolic AI (for explainable reasoning) offers a more trustworthy approach.
- Under-the-hood panels: These provide a visual way to trace the reasoning process of agents, making their actions more transparent to users.
- Tailored explanations: LEIAs are designed to tailor explanations to different users based on their needs and understanding, highlighting the importance of factors like "mindreading" in multi-agent communication.