Shawn Wang: recommendation

Transcript context

…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. I mean, technically, now you can use Gemini as a judge, Gemini is touting a lot of their capabilities and low resource languages. One more thing before on that sort of data topic, did you have any exploration of synthetic data at all? You know, use Mistral to rephrase some existing part of your data sets, generate more tokens, anything like that, or any other form of synthetic data that you choose to mention? I think you also mentioned the large world model paper, right? We used GPT-4 to rephrase certain aspects of the chat data, reformatting it or kind of generating new types of tokens and language and types of data that the model could see. And also like trying to take the lower probability, right, or the lower correlated instances of out of domain data in that we wanted to inject it to the model too, as well. So I actually think a lot of the moat is in the data pipeline. You'll notice most papers just don't really go into deep detail about the data set creation because, I mean, there's some aspects that are uninteresting, right? Which is like, we paid a bunch of people and generated a lot of good data. But then the synthetic data generating pipeline itself, sometimes that could be like 25% or 50% of the entire data set that you've been used to depreciating.…

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