There are still significant limitations, no amount of prompting will get current models to approach abstraction and architecture the way a person does. But I'm finding that these Gemini models are finally able to replace searches and stackoverflow for a lot of my day-to-day programming.
Are we sure they know these things as opposed to being able to consistently guess correctly? With LLMs I'm not sure we even have a clear definition of what it means for it to "know" something.
What is the practical difference you're imagining between "consistently correct guess" and "knowledge"?
LLMs aren't databases. We have databases. LLMs are probabilistic inference engines. All they do is guess, essentially. The discussion here is about how to get the guess to "check itself" with a firmer idea of "truth". And it turns out that's hard because it requires that the guessing engine know that something needs to be checked in the first place.
Knowing it's correct. You've just instructed it not to guess remember? With practice people can get really good at guessing all sorts of things.
I think people have a serious misunderstanding about how these things work. They don't have their training set sitting around for reference. They are usually guessing. Most of the time with enough consistency that it seems like they "know'. Then when they get it wrong we call it "hallucinations". But instructing then not to guess means suddenly they can't answer much. There no guessing vs not with an LLM, it's all the same statistical process, the difference is just if it gives the right answer or not.
I love the convergence with philosophy here.
This is the second reply that's naively just asserted a tautology. You can't define "knowledge" in terms of "knowing" in the sense of the English words; they're the same word! (You can, I guess, if you're willing to write a thesis introducing the specifics of your jargon.)
In point of fact LLMs "know" that they're right, because if they didn't "know" that they wouldn't have told you what they know. Which, we all agree, they do know, right? They give answers that are correct. Usually.
Except when they're wrong. But that's the thing: define "when they're wrong" in a way rigorous enough to permit an engineering solution. But you really can't, for the same reason that you can't prevent us yahoos on the internet from being wrong all the time too.