Mark Huang: evaluation

Transcript context

…Is there a ratio between diversity of the data set versus diversity compared to what the model already knows? Like does the model already need to understand a good part of the new like the context extension data to function? Like can you put context extension data set that is like very far from like what was in the pre training? I'm just thinking as as the model get older, some of the data sets that we have might not be in the knowledge of the existing model that you're trying to extend. I think that's always a consideration. I think specifically, you really got to know how many tokens were expended into that particular model from the start. And all models these days are now double digit trillions, right? So it's kind of a drop in the bucket, if you really think I can just put, you know, a billion tokens in there. And I actually think that the model is going to truly learn new information. There is a lot of research out there between the differences with respect to full fine tuning, which we applied full fine tuning versus lower base fine tuning. It's a trade off. And my opinion of it is actually that you can test certain capabilities and you can kind of inject new knowledge into the model. But to this day, I've not seen any research that does like a strong, well scaled out empirical study on how do you increase the model's ability to understand like these decision boundaries with a new novel data. Most of it is holding on a portion of the data as like novel and then needing to recycle some of the old knowledge. So it just doesn't forget and get worse at everything else, right? Which was seen. We do have historical precedent, where the original code bomb was trained further from Mama 2, and it just lost all its language capability, basically, right? So I don't want to call that project like deem it as a failure, but it wasn't a really successful generalization exercise, because, you know, these models are about flexibility and being like generic to a certain extent. One thing I see in the recent papers that have been coming out is this sort of concept of multi-stage training data. And if you're doing full fine tuning, maybe the move or the answer is don't train 500 billion tokens on just code, because then yeah, it's going to massively overfit to just code. Instead, maybe the move is to slowly change the mix over the different phases, right? So in other words, you still need to mix in some of your original source data set to make sure it doesn't deviate too much. I feel like that is a very crude solution. Maybe there's some smarter way to adjust like the loss function so that it doesn't deviate or overfit too much to more recent data. It seems like it's a solvable thing. That's what I'm saying. Like this overfitting to more recent data issue.…

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