Shawn Wang: evaluation

Transcript context

…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. Yeah, I do think solvable is hard. I think provably solvable is always something that I know is extremely difficult, but from a heuristical standpoint, as well as like having like some sort of statistical efficiency on like how you can converge to the downstream tasks and improve the performance that way in a targeted manner, I do think there are papers that try to do that. Like the Do-Re-Mi paper, I think it was released last year, it was really good about doing an empirical study on that. I think the one thing people struggle with though, is the fact that they always try to do it on pretty naive tasks. Like you target like a naive task, and then you create your data mixture and you try to show some sort of algorithm that can retain the performance for those downstream tasks. But then what do we all care about are actually like really, really interesting, complex tasks, right? And we barely have good evaluations for those. If you do a deep dive at the Gemini 1.5 technical paper, which they just updated, it was a fantastic paper with new updates. If you look at all of their long context evaluations there, like a lot of them are just not something that the open community can even do, because they just hired teachers to evaluate whether or not this model generated a huge lesson plan that is really coherent. Or like you hire a bunch of subject matter experts, or they taught the model how to do language translation for extinct language where only 200 people in the world know. It's kind of hard for us to do that same study as an early stage startup.…

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