A Geometric Method to Spot Hallucinations Without an LLM Judge

A novel geometric method has been developed to detect hallucinations in large language models (LLMs) without relying on an external LLM judge. Inspired by the decentralized coordination observed in bird flocks, this approach analyzes the local consistency of model outputs to identify deviations indicative of hallucinations, thereby enabling more reliable and interpretable AI systems. This technique leverages geometric principles to assess the coherence of generated responses, allowing for the detection of false or misleading information without additional language model evaluations. By focusing on the intrinsic structure of the output space, the method offers a scalable and efficient way to improve the trustworthiness of