A Scalable Entity-Based Framework for Auditing Bias in Large Language Models
This framework uses named entities as controlled probes to systematically measure and quantify bias in large language models across various domains like politics and economics. It defines entity bias as prediction change caused by substituting an entity, allowing for rigorous, scalable auditing of model reliance on specific names before high-stakes deployment.