Mark Huang: recommendation

Transcript context

…And does this change in your mind for very long chats versus a lot of documents? The chat is kind of interactive, you know, and information changes. The documents are just trying to synthesize more and more things. Yeah. Any thoughts on how those two workloads differ? I would say like with the document aspect of things, you probably have a little bit more ability to tweak other methodologies. You can get around the long context sometimes where you can do retrieval augmented generation or you do hierarchical recursive summarization, whereas evolution in like a session, because that state variable could undergo pretty rapid changes. It's a little bit harder to you getting around that without codifying a really specific workflow or like some sort of state clause that is going back to like determinism. Right. And then finally, what I really think people are trying to do is figure out how did all these shots progress over time? How do you get away from the brittleness of the retrieval step? If you shove in a thousand shots or 2000 shots, will it just make the retrieval aspect of good examples irrelevant? Kind of like a randomly sampling is fine at that point. There's actually a paper on that that came out from CMU that they showed with respect to a few extraction or classification, high cardinality benchmarks, they tracked fine tuning versus in context learning versus many, many shot in context learning. And they basically showed that many, many shot in context learning helps to prevent as much sensitivity around the examples themselves, right? Like the distraction error that a lot of LLMs get where you give it irrelevant context and it literally can't do the task because it gets sort of like a person too, right? Like you got to be very specific about, I don't want to distract this person because then they're going to go down a rabbit hole and not be able to complete the task. Yeah. Well, that's kind of the flip side of the needle in a haystack thing too in a bit. It's like now the models pay attention to like everything so well. Like sometimes it's hard to get them to like, I just said that once, please do not bring that up again. You know, it happens to me with code. Yeah. It happens to me with like CSS style sometimes or like things like that. If I have a long conversation, it tries to always reapply certain styles, even though I told it maybe that's not the right way to do it. But yeah, there's a lot again of empirical that people will do. And just, I know we kind of went through a lot of the technical side, but maybe the flip side is why is it worth doing? What are like the use cases that people have that make long context really useful? I think you have a lot of healthcare use cases. I saw on your Twitter, you just mentioned the finance use case, obviously some of the filings and documents that companies publish can be quite worthy. Any other things that you want to bring up, maybe how people are using gradient, anything like that, I think that will help have a clearer picture for people. Yeah.…

Stored transcript either side of the excerpt. The highlighted words are the published quote; the surrounding text is unedited source, never generated.