I’m betting the truth is somewhere in between, models are only as good as their training data – so over time if they prune out the bad key/value pairs to increase overall quality and accuracy it should improve vastly improve every model in theory. But the sheer size of the datasets they’re using now is 1 trillion+ tokens for the larger models. Microsoft (ugh, I know) is experimenting with the “Phi 2” model which uses significantly less data to train, but focuses primarily on the quality of the dataset itself to have a 2.7 B model compete with a 7B-parameter model.
I’m betting the truth is somewhere in between, models are only as good as their training data – so over time if they prune out the bad key/value pairs to increase overall quality and accuracy it should improve vastly improve every model in theory. But the sheer size of the datasets they’re using now is 1 trillion+ tokens for the larger models. Microsoft (ugh, I know) is experimenting with the “Phi 2” model which uses significantly less data to train, but focuses primarily on the quality of the dataset itself to have a 2.7 B model compete with a 7B-parameter model.
https://www.microsoft.com/en-us/research/blog/phi-2-the-surprising-power-of-small-language-models/
This is likely where these models are heading to prune out superfluous, and outright incorrect training data.