Mark Huang: recommendation

Transcript context

…And so once you had the technical discovery, what about the actual customer interest, customers that you work with? I feel like sometimes the context size can be a bit of a marketing ploy, you know, people are like, oh, yeah, well, no, 1 million, 2 million, 3 million, 4 million. That's kind of the algorithms side of it. How do you power the training? But the other side is obviously the data that goes into it. There's both quantity and quality. I think that's how one of your tweets, you trained on about 200 million tokens for the AP model to the context extension. But what are the tokens? You know, how do you build them? What are like maybe some of the differences between pre-training data sets and context extension data sets? Yeah, any other color you give there will be great. So specifically for us, we actually staged two different updates to the model. So our initial layer that we trained was just basically like a pre-training layer. So continual pre-training where we took the slim pajamas data, and then we filtered it and concatenated it so that it would reach the context lengths that we were trying to extend out to. And then we took the UltraChat data set, filtered it down, or maybe some other, you know, second order derivative of the UltraChat data set that was curated in, and then filtered it down and then reformatted it for our chat use case. For those two data sets, you always have to really keep in mind for the pre-training data, whether or not you may be like cutting off tokens in weird ways, whether or not, you know, the content is actually diverse enough to retain the ability of the model. So slim pajamas tends to be one of the best ones, mostly because it's a diverse data set. And you can use embeddings too as a pre-filtering step as well, right? Like how diverse are your embeddings space to the original corpus of the model, and then train on top of that to retain its abilities. And then finally, for the chat data set, making sure that it's attending to all the information that would be expected to really stretch its capabilities, because you could create like a long context data set where every single time the last 200 tokens could answer the entire question, and that's never going to make the model attend to anything. So it's even something that we're doing right now is trying to think about like, how do we actually improve these models? And how do you ablate the data sets such that it can expose like even more nuanced capabilities that aren't easily measurable quite yet? 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.…

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