Towards Data Science is a community-powered publication that showcases work in data science, machine learning and artificial intelligence. Every day newcomers, seasoned researchers and industry practitioners publish tutorials, research notes and real-world case studies that help the field move forward.

Towards Data Science

A novel geometric method called Displacement Consistency (DC) detects LLM hallucinations by analyzing the directional patterns of question-answer pairs in embedding space, without requiring another LLM as a judge. The approach treats embeddings as vectors with direction and magnitude, observing that grounded responses within a domain produce consistent displacement angles while hallucinations deviate. DC achieves near-perfect discrimination (AUROC 1.0) across five embedding models on benchmarks like HaluEval and TruthfulQA, but requires domain-specific calibration sets of ~100 examples since grounding patterns are locally consistent but globally variable across different domains.

A Geometric Method to Spot Hallucinations Without an LLM Judge

The problem we’re actually trying to solve