principles.fyi · the brain · concept

data contamination

When test questions leak into the training data, so the model has secretly already seen the answers.

test question ∈ training data -> inflated, untrustworthy score

Data contamination happens when the examples used to evaluate a model accidentally appear in its pretraining data — because both came from the same giant web scrape. The model then looks brilliant on that test, but it may just be recalling answers it memorized, not reasoning. It's a core reason benchmark scores can be misleading, and it ties pretraining data directly to evaluation: you can't fully trust a score unless you know the questions weren't in the training set. (More on this in the evaluation chapter.)