There is probably some limit where making the dataset larger, with more diverse information, does not create meaningful improvements with current architectures. I do not know what that limit is or what it looks like, but I also don’t think we are particularly close to it yet.
“The Pile” dataset is the asset we needed to jumpstart this process, it had so much raw data it could get us over the hump, but Phi and some of the models trained on explicit reasoning make the limitations of random shit people say on the internet pretty clear.
I'm bullish on domain specific models that start from generalized models. Something of a T shape analogy, but maybe a couple of distillation & fine-tuning steps
I disagree with this. If you give GPT information that was not part of its dataset and ask it to make question and answer pairs off of that information, you are adding higher quality breadth to the training corpus.
“The Pile” dataset is the asset we needed to jumpstart this process, it had so much raw data it could get us over the hump, but Phi and some of the models trained on explicit reasoning make the limitations of random shit people say on the internet pretty clear.