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The original was posted on /r/machinelearning by /u/meltingwaxcandle on 2025-02-20 21:22:44+00:00.

LLM hallucinations are a major challenge, but what if we could predict when they happen? Nature had a great publication on semantic entropy, but I haven’t seen many practical guides on detecting LLM hallucinations and production patterns for LLMs.

Sharing a blog about the approach and a mini experiment on detecting LLM hallucinations. BLOG LINK IS HERE

1. Sequence log-probabilities provides a free, effective way to detect unreliable outputs (~LLM confidence).
2. High-confidence responses were nearly twice as accurate as low-confidence ones (76% vs 45%).
3. Using this approach, we can automatically filter poor responses, introduce human review, or iterative RAG pipelines.

Love that information theory finds its way into practical ML yet again!

Bonus: precision recall curve for an LLM.