Training Data Provenance Checker

Nearly every question about a model reduces to its training data

Is the output infringing, is there personal data in it, are the benchmark scores real, why does it fail on this group, can it be deployed in this jurisdiction: all of these resolve to what the model was trained on. An organisation that cannot enumerate its training corpus cannot answer any of them, and the questions arrive together, usually in response to a complaint rather than in advance. Provenance work is unusually front-loaded because after training the only remedy for a bad dataset is retraining.

Contamination makes performance claims void, not merely optimistic

If an evaluation set appears in the training data, the resulting score measures recall of memorised examples rather than capability, and every decision made on the strength of that score rests on nothing. This is common enough in publicly assembled corpora that silence on the question is itself informative, and the check is not expensive: hashing evaluation examples against the corpus catches the bulk of it.

Lineage from checkpoint to corpus is what makes any of it usable

Documentation that describes datasets in general, without recording which exact version of which dataset produced a given checkpoint, cannot answer a question about the model that is actually deployed. That is the only model anyone asks about. A manifest per training run costs almost nothing at the time and is the difference between answering a question and reconstructing a guess.

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