Alex Volkov: preference

Transcript context

…All right. Alex, do you have your hand up? And then I wanted to dig more on the the preference data that Nathan mentioned. Mm-hmm. Hey guys. Just to introduce myself real quick, I'm Alex. We participant in the spaces is, and my angle and the way I vibe, quote unquote vibe check models is via languages. And to me, it was really surprising that they released kind of the second iteration while also knowing how much meta actually does for translation. They have very famous N L L B models, no language left behind. They released the world models that you can speak in multiple, like a thousand languages that understands, and for some reason, they're open source models. They are not very strong multilingually. So we've seen this with GPT4, which was way better at multilingual speak. Claude highlighted this point with Claude two that is like way better at the blue score. I think for, for languages, and I've tried and my go-to like vibe check with these models is to, with the, especially the open source one is the ability to translate, the ability to understand the languages. I've tried it with, with Hebrew a little bit. I've tried with. Very, very impressed. Now, obviously fine tuning will come and obviously people will fine tune these, these models towards different outcomes, but it's very interesting considering how much meta does elsewhere for languages and to bring the world together. How much kind of this model did not focus on this, this specific kind of issue. And the, the, the second thing is also code. I know you guys talked about human eval. That's fairly low in terms of the score out of the box. And obviously fine tuning will, will, will make it better, but fairly, fairly disappointing score on, on human ev, right? Fairly low coding abilities. And we've seen previously that there's some assumption that training on more code in your dataset actually gives you better kinda logic and reasoning abilities. So kind of surprised that that was fairly low. We went to chairman with these two, two examples about Lama. I'll say on the human eval piece don't count it, not just yet. So I've, I've had some dms with Quinn Slack or of source graph, and he's is you know, very actively building Cody their, their coding assistant bot. And it's well known that human eval is not a very good or reflective measure of how we use coding chatbots. And so like, it, it is probably human EV emails is probably overrepresented in terms of being, being like this effectively the sole benchmark by which we value code models. We, we just need new benchmarks for code.…

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