Mike Conover: prediction

Transcript context

…Yeah, exactly. How did you think about the evolution of context sizes as you built the company and where we are today? What are things that people get wrong? Any commentary there? Sure. We very much take a systems of systems approach. When I started the company, I think I had more faith in the ability of large context windows to generally solve problems relating to synthesis. And actually, if you think about the attention mechanism and the way that it computes similarities between tokens at a distance, I, on some level, believed that as you would scale that up, you would have the ability to simultaneously perceive and draw conclusions across vast, disparate bodies of content. And I think that does not empirically seem to be the case. So when, for example, you, and this is something anybody can try, take a very long document, like needle in a haystack. I think, sure, we can do information retrieval on specific fact-finding activities pretty easily. I kind of think about it like summarizing, if you write a book report on an entire book versus a synopsis of each individual chapter, there is a characteristic output length for these models. Let's say it's about 1,200 tokens. It is very difficult to get any of the commercial LLMs or LLAMA to write 5,000 tokens. And you think about it as, what is the conditional probability that I generate an end token? It just gets higher the more tokens are in the context window prior to that sort of next inference step. And so if I have 1,000 words in which to say something, the level of specificity and the level of depth when I am assessing a very large body of content is going to necessarily be less than if I am saying something specific about a sub-passage. I mean, if you think about drawing a parallel to consumer internet companies like LinkedIn or Facebook, there are many different subsystems with it. So let's take the Facebook example. Facebook almost certainly has, I mean, you can see this in your profile, your inferred interests. What are the things that it believes that you care about? Those assessments almost certainly feed into the feed relevance algorithms that would judge what you are, you know, am I going to show you snowboarding content? I'm going to show you aviation content. It's the outputs of one machine learning system feeding into another machine learning system. And I think with modern rag and sort of agent-based reasoning, it is really about creating subsystems that do specific tasks well. And I think the problem of deciding how to decompose large documents into more kind of atomic reasoning units is still very important. Now, it's an open question whether that is a model that is addressable by pre-training or instruction tuning. Like, can you have synthesis-oriented demonstrations at training time? And now this problem is more robustly solved because synthesis is quite different from complete the next word in the great Gatsby. I think empirically is not the case that you can just throw all of the SCC filings in a million token context window and get deep insight that is useful out the other end. Yeah. And I think that's the main difference about what you're doing. It's not about summarizing. It's about coming up with different ideas and kind of like thought threads to pull on.…

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