CoE: Collaborative Entropy for Uncertainty Quantification in Agentic Multi-LLM Systems
📰 ArXiv cs.AI
Collaborative Entropy (CoE) is a metric for uncertainty quantification in multi-LLM systems, addressing semantic disagreement across models
Action Steps
- Define a shared semantic cluster space across multiple LLMs
- Calculate intra-model semantic uncertainty
- Calculate inter-model semantic disagreement
- Combine intra-model and inter-model uncertainties using CoE metric
Who Needs to Know This
AI engineers and researchers working on multi-LLM systems can benefit from CoE to better understand and quantify uncertainty in their models, enabling more accurate and reliable predictions
Key Insight
💡 CoE captures semantic disagreement across models, providing a more comprehensive understanding of uncertainty in multi-LLM systems
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🤖 Introducing CoE: a unified metric for uncertainty quantification in multi-LLM systems 📊
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