How can I build a reliable multi-agent fact-checker?
Multi-Agent Fact Checking
This paper tackles the problem of automated fact-checking using multiple unreliable AI agents (like LLMs). It proposes a method to estimate each agent's individual error rate (unreliability) over time, without needing to know the true answers in advance. The core idea involves observing the agents' agreements and disagreements and updating the reliability estimates accordingly. This is relevant to LLM-based multi-agent systems because it offers a way to improve overall system accuracy by dynamically weighting individual LLM outputs based on their learned trustworthiness. It shifts the paradigm from relying on pre-defined LLM confidence scores to dynamically learning LLM reliability based on observed performance in a multi-agent setting.