Is it though?
There's nuance in what we call knowledge, but to me, knowledge is not uni-dimensional. What I mean by that is, I know when I _know_ something, and I have learned that that is not a trivial skill.
How do I know I _know_ something? Usually because I have searched for evidence from multiple angles / dimensions / senses. When multiple independent observations of quality agree, and my conclusion matches the result, it is safe to say that I know that fact.
An LLM may have multiple 'dimensions' in a linear algebra sense, but does it have independent information?
> How do I know I _know_ something? Usually because I have searched for evidence from multiple angles / dimensions / senses. When multiple independent observations of quality agree, and my conclusion matches the result, it is safe to say that I know that fact.
I wanted to thank you for laying out this perspective. In my experience, in discussions about LLMs, a lot of time people get caught up on words like 'know' which work well for talking about humans, but don't cash them out in definitions I know what to do with. This is a really content-ful of sharing what you mean by your claim.
This is technically true, but no one actually does much of it consciously. But this is how you picked up every skill and understanding, including language, since you've been born.
And this - the unconscious fusion of correlated patterns - is pretty much what LLMs undergo in training.
EDIT:
Also, a lot of confusion is created by using terms like "a fact" or "a conclusion" or "a result", as if they were fixed points in space, fully defined mathematical symbols. But there exist no such thing. There is no binary "know/don't know", there is no binary "result match/does't match the conclusion". Those are all continuous quantities, that get rounded off for convenience.
Correct. But it does mean they're about "language" rather than knowledge. It's meant to read, parse and generate language, no matter if it includes prose, facts, knowledge or whatever.
> Clearly, they aren't as likely to generate "Colorless green ideas sleep furiously" as "It's important to feed your dog".
Clearly, as the training set probably contains much more instances of the latter, than the first example. And if it's more probable to generate, that's the text it'll generate. It doesn't understand why or how, just that it's more likely, so that's the way it's going.
Not really. It was invented because they were trained on natural language corpuses. It wasn't a deep, philosophical concept.
It continues to be used for models that are doing completely non-languagy stuff, such as robotics, although I expect the growing use of 'foundation model' or some other less-confusing term will likely replace it.
If it wasn't, what would we be doing here?
To some extent, those are kind of the same thing.
They are not the same thing. They are not even "kind of" the same thing, for any reasonable definition of "kind of." That should be obvious, because a lie is language, which is pretty close to the opposite of knowledge.
I mean no offense, but your hedging words are doing so much work that I think your statement counts as bullshit.
Edit: thinking about it more, I think your comment shows the particular kind of bullshit that often characterizes tech people's thinking and is so useful for hyping shit (e.g. take something hard, lossily reformulate it into something easier but vaguely similar, solve the easy thing, then declare you solved the hard thing).
Knowledge is... concepts and their relationships, typically with the implication of being grounded in objective reality (although one can be knowledgeable about things like, say, Tolkien, or Star Wars, or fantasy tropes more generally).
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Language is (a particular way of representing) knowledge, in about the same way that a particular .cpp file is (a particular way of representing) a linked list or whatever.
So, in your view, are .cpp files and linked lists "kind of" the same thing?
Language can be used to express some kinds of knowledge, but they're not even "kind of" synonymous. Language can express a lot of things that aren't knowledge, and there's knowledge that can't be expressed with language (qualia, at least).
All the more and less formal models we invent for describing "language"? This is just fitting curves to a snapshot of reality. So sure, "a lie is language, which is pretty close to the opposite of knowledge" - a lie can be expressed in the formal model. But the truth (as subjectively understood) is much more likely to be spoken or written down in practice.
Communication isn't random. Language isn't random. Learning it is picking up the patterns.
>>>> But it does mean they're about "language" rather than knowledge.
>>> To some extent, those are kind of the same thing.
>> They are not the same thing.
> Of course they are the same thing.
Language and knowledge (both broadly construed) are obviously not the same thing. Language can encode non-knowledge like nonsense or lies, and there is knowledge (e.g. of the experience of qualia) that can't be expressed in language.
I think the point up-thread is true: something that knows only about how language is unreliable source of knowledge. Even if all its input is true knowledge, it can still blindly combine that input in linguistically plausible but false ways.
you can use language as a proxy for knowledge in some cases, but doing so guarantees generating bullshit, which is the entire point of the OP.
it does mean they're about "language" rather than knowledge
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.
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.
Obviously, they're better at grammar than many other things, but it's clear that they contain a vast amount of knowledge. They contain the information that the sky is blue and firetrucks are red, which isn't a matter of grammar or coherence, but is simply information about the world.