Why AI Confidence Scores Don't Reliably Detect Hallucinations

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A language model is considered well-calibrated if its stated confidence levels match its actual accuracy rates — for instance, predictions made at 70% confidence should be correct roughly 70% of the time. Researchers, notably Guo et al. in a 2017 paper, established that modern neural networks trained for high accuracy tend to be significantly overconfident, though a technique called temperature scaling can largely correct this without affecting accuracy. Calibration is distinct from discrimination, meaning a model can be perfectly calibrated yet still be practically useless if it simply predicts average base rates. If language models were reliably self-calibrated, hallucination detection would be straightforward — low-confidence responses could be flagged and routed to humans or external search tools. A 2022 Anthropic study by Kadavath et al. found that large models show reasonable calibration on multiple-choice tasks, but this does not fully translate to open-ended generation, which is where hallucination detection remains an unsolved challenge.

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