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> How many examples does OpenAI train on now that are just variants of counting the Rs in strawberry?

Well, that's easy: zero.

Because even a single training example would 'solved' it by memorizing the simple easy answer within weeks of 'strawberry' first going viral , which was like a year and a half ago at this point - and dozens of minor and major model upgrades since. And yet, the strawberry example kept working for most (all?) of that time.

So you can tell that if anything, OA probably put in extra work to filter all those variants out of the training data...

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SweetSoftPillow a day ago | parent [-]

No, just check their models Knowledge cutoff dates

	▲	gwern 15 hours ago \| parent [-]
		Nope! The knowledge cutoff does not show lack of leakage. Even if you get a non-confabulated cutoff which was before anyone ever asked the strawberry question or any question like it (tokenization 'gotchas' go back to at least davinci in June 2020), there is still leakage from the RLHF and tuning process which collectively constitute post-training, and which would teach the LLMs how to solve the strawberry problem. People are pretty sure about this: the LLMs are way too good at guessing things like who won Oscars or Presidential elections. This leakage is strongest for the most popular questions... which of course the strawberry question would be, as it keeps going viral and has become the deboooonkers' favorite LLM gotcha. (This is, by the way, why you can't believe any LLM paper about 'forecasting' where they are just doing backtesting, and didn't actually hold out future events. Because there are way too many forms of leakage at this point. This logic may have worked for davinci-001 and davinci-002, or a model whose checkpoints you downloaded yourself, but not for any of the big APIs like GPT or Claude or Gemini...)