How to Evaluate a Hallucination Detector

Sat, 29 Aug 2026 23:14:00 +0100

Chapter 5 gave us something concrete enough to evaluate.

Hallucination Energy has:

a target property
an observable proxy
a mathematical definition
an implementation
continuous outputs
natural-task signal
adversarial failures

That sounds like progress.

It is also the point where it becomes dangerously easy to fool ourselves.

Suppose a detector reports:

AUC = 0.75

Is that good?

Perhaps.

But we still do not know:

which class was called positive
how hard the negatives were
whether train and test shared source documents
whether they shared entities or semantic neighborhoods
what prevalence the deployment actually has
whether the labels were trustworthy
how wide the confidence interval was
what happens at 1% false acceptance
how uncertain that 1% estimate is
whether the threshold transfers to another domain
whether the score is a probability at all
whether the verifier itself failed upstream
whether the generator changed
whether decoding parameters changed
whether the model collapses on structural adversaries

A single number cannot answer those questions.

Precision Recall on Programmer.ie: Modern AI programming

How to Evaluate a Hallucination Detector