[nabakin]

I think the only way is to check your dataset for the benchmark leak and remove it before training, but (as you say) that's assuming an honest actor is training the LLM, going against the incentives of leaving the benchmark leak in the training data. Even then, a benchmark leak can make it through those checks.

I think it would be interesting to create a dynamic benchmark. For example, a benchmark which uses math and a random value determined at evaluation for the answer. The correct answer would be different for each run. Theoretically, training on it wouldn't help beat the benchmark because the random value would change the answer. Maybe this has already been done.