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A Geometry-Based Hallucination Check That Skips the LLM Judge

A new write-up proposes detecting hallucinations by comparing the direction of question-to-answer embedding shifts against nearby grounded examples. The method is reported to hit perfect separation on multiple benchmarks without using an LLM-as-judge.

Javier Marin shared a Towards Data Science write-up (Jan 17, 2026) on Displacement Consistency (DC), a geometry-based way to flag LLM hallucinations without an LLM-as-judge.

DC looks at the direction of the embedding shift from question → answer, scoring alignment via cosine similarity.

How it works:

  • Build a domain-specific set of grounded Q–A pairs
  • For a new query, retrieve nearby questions
  • Compute the neighbors’ mean displacement direction
  • Score how closely the new answer’s displacement matches it

Reported results:

  • Tested across 5 embedding models: all-mpnet-base-v2, e5-large-v2, bge-large-en-v1.5, gtr-t5-large, nomic-embed-text-v1.5
  • AUROC = 1.0 on a synthetic benchmark for all five models
  • Also reports perfect separation on:
    • HaluEval-QA
    • HaluEval-Dialogue
    • TruthfulQA
  • No source documents required at inference time