I think it's more likely that LLMs are actually learning and understanding concepts as well as memorizing useful facts, than that LLMs have discovered a compression method with that high of a compression ratio, haha.
LLMs are just completing patterns of text that have been given before, 'everthing ever written' is both a lot for any individual person to read; but also, almost nothing, in that to propertly describe a table requires more information
text is itself an extremely compressed medium which lacks almost any information about the world; it succeeds in being useful to generate because we have that information and are able to map it back to it
I should make it clear that my comparison there is unfair and mostly just funny – you don't need to store every possible combination of 10 tokens, because most of them will be nonsense, so you wouldn't actually need that much storage. That being said, it's been fairly solidly proven that LLMs aren't just lookup tables/stochastic parrots.
Well i'd strongly disagree. I see no evidence of this; I'm am quite well acquainted with the literature.
All empirical statistical AI is just a means of approximating an empirical distribution. The problem with NLP is that there is no empirical function from text tokens to meanings; just as there is no function from sets of 2D images to a 3D structure.
We know before we start that the distributions of text tokens are only coincidentally related to the distributions of meanings. The question is just how much value that coincidence has in any given task.
(Consider, eg., that if I ask, "do you like what i'm wearing?" there is no distribution of responses which is correct. I do not want you to say "yes" 99/100, or even 100/100 times. etc. what I want you to say is a word caused a mental state you have: that of (dis)liking what i'm wearing.
Since no statistical AI systems generate outputs based on causal features of reality, we know a priori that almost all possible questions that can be asked cannot be answered by LLMs.
They are only useful where questions have cannonical answers; and only because "cannonical" means that a text->text function is likely to be conidentally indistinguishable from a the meaning->meaning function we're interested in).
I’m not saying you believe that, but I fail to see how that situation is structurally different from what you claim. If it’s a matter of degree, how do you feel things change as the situation becomes more complex?
But, to be clear, the reason it could ever work at all has nothing to do with the methods or the data itself, it has to do with the properties of the data generating process (ie., reality, ie., what's being measured).
You can never build representations from measurement data, this is called inductivism and it's pretty clearly false: no representation is obtained from just characterising measurement data. Theres no cases where I can think of that this would work -- temperature isnt patterns in thermometers; gravity isnt patterns in the positions of stars; and so on.
Rather you can decide between competing representations using stats in a few special cases. Stats never uncovers hidden representations, it can decide between different formal models which include such representations.
eg., if you characterise some system as having a power-law data generating process (eg., social network friendships), then you can measure some parameters of that process
or, eg., if you arrange all the data to already follow a law you know (eg., F=Gmm/r^2) then you can find G, 'statistically'.
This has caused a lot of confusion histroically: it seems G is 'induced over cases', but all the representaiton work has alerady been done. Stats/induction just plays the role of fine-tuning known representatios. it never builds any
If there were such a thing, it'd be interesting to propose how our own minds, at least to the degree that they can be seen as statistical learners in their own right, achieve semantics. And how that thing, whatever it might be, is not itself a learned representation driven by statistical impression.
We move, and in moving, grow representations in our bodies. These representations are abstracted in cognition, and form the basis for abductive explanations of reality.
We leave plato's cave by building vases of our own, inside the cave, and comparing them to shadows. We do not draw outlines around the shadows.
This is all non-experimental 'empirical' statistics is: pencil marks on the cave wall.
This is like saying a brain operates wholly on electrochemical states and knowns nothing about meaning in the real world, though; the mechanistic description is accurate, the cognitive conclusion attached to it is, at best, based on unsupported conjecture about the relation of mechanism to understanding.