Knowing the nature of a test ahead of time, building out your capabilities and tooling before entering the exam hall when your peers don't have that advantage, makes you a cheater.
Lots of people doing the same with extra steps (generating synthetic data from test questions with the LLM then training on it)
I wish we'd move past public test sets for LLM benchmarks: publish a plain english explanation of the tasks, allow questions and clarifications, and but never release a single question from the test set verbatim.
It made sense back when models needed to be finetuned on the task to even reliably answer. If we're saying this is the path to AGI we should be able to rely on the generalization of the model to get it right.
If a model was trained on <|begin_text|> <|end_text|> and you change the tokens passed to <|start_text|> <|end_text|>, it loses several 'IQ points' if it can even answer back at all anymore.
Synthetic data is fine. Synthetic data on very similar questions generated based on the description is typically fine. But once the shape of what you're training on gets too close to the actual holdout questions, you're getting an uplift that's not realistic for unseen tasks.
I wish we'd move past public test sets for LLM benchmarks: publish a plain english explanation of the tasks, allow questions and clarifications, and but never release a single question from the test set verbatim.
It made sense back when models needed to be finetuned on the task to even reliably answer. If we're saying this is the path to AGI we should be able to rely on the generalization of the model to get it right.