Unmasking Conversational Bias in AI Multiagent Systems

Detecting biases in the outputs produced by generative models is essential to reduce the potential risks associated with their application in critical settings. To address this gap, we present a framework designed to quantify biases within multi-agent systems of conversational Large Language Models (LLMs). Our approach involves simulating small echo chambers, where pairs of LLMs, initialized with aligned perspectives on a polarizing topic, engage in discussions.