Most hallucination detection approaches tell you to train another model. I did not want to do that. I...
Four statistical signals — length ratio, unknown word rate, QA overlap, numeric inconsistency — achieve 96% recall on LLM hallucination detection across 10,000 HaluEval examples with 50 lines of Python, no GPU or external API. A tunable threshold and per-signal explainability make it production-viable at scale where model-on-model approaches like SelfCheckGPT are compute-prohibitive.
Most hallucination detection approaches tell you to train another model. I did not want to do that. I used four statistical signals, a combined score, and a tunable threshold. No fine-tuning. No GPU. No external API. Tested on 10,000 real examples from the HaluEval dataset.
Soft flag result: precision 0.71, recall 0.96.
Strict flag result: precision 1.00, recall 0.38.
Here’s how it works.
Why Not Just Use a Model?
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I do a lot of research. Legal documents, technical specs, academic papers, regulatory filings....
