FWIW, although 'justified true belief' is classic in the sense of being an old definition of knowledge, I don't think any significant number of modern philosophers would use it.
But yeah the problem what you mean by "knowing." For example, I am perfectly happy to say that a LLM "knows" words just like I "know" words, or that it "knows about" very popular topics in a loose sense.
But that's a very different sense of "knows" from the kind of specific propositional knowledge we're usually interested in when using a LLM as a search engine, fact checker or source of information!
I’m clearly keeping track of how strongly I believe a variety of things. Not how I started believing them to begin with, however, so I lack the justification. And in a lot of cases, the lost justification would have been: “Because someone else said it, so now I’m aping them.”
But I think almost none of them would ascribe "knowledge" to a statistical engine that output propositions of random truth values weighted by how many examples of that proposition it's seen in it's training data.
You can view the logprobs in the API and literally watch it choose to say different things based on it's internal dice rolls.
Knowledge without a feedback mechanism for verification is prone to veer off course.
Right now the feedback mechanism is based on human reinforcement.
What if we put an LLM in an android’s body and let the machine interact with the world through the same senses that we do?
Ultimately though this is all about the definition of words. Humans find the distinction between "true" and "false" to be important and meaningful; animals don't care. AIs don't care (being incapable of caring), but humans care very much about the propositional veracity of statements that AI makes.
The important thing is that we're on the same page about what's actually happening with these systems and how they work and don't slip into magical thinking or assume that just because a system is facile with language it automatically has a sense of truth or right or wrong.
This is only going to get more complicated as models get more sophisticated.
https://www.youtube.com/watch?v=yhfl7kasjZc
There are a quite a few videos with the same reaction. There does seem to be something going on.
They can emit statements that happen to be true because the probabilities fall that way (as a sibling comment points out.) But it's not "justified", because models don't have any ability to remember or cite sources, or even internally classify tokens as true or false (it's all just weights.)
(And it's not a "belief", either, because there's no intentionality, but that's a more slippery concept.)
Therefore, philosophically speaking, it is fundamentally impossible to acquire knowledge from a LLM unless you verify ("justify") a statement via some secondary, more trustworthy channel.