LLMs are absolutely not "fully understood". We understand how the math of the architectures work because we designed that. How the hundreds of gigabytes of automatically trained weights work, we have no idea. By that logic we understand how human brains work because we've studied individual neurons.

And here's some more goalpost-shifting. Most humans aren't capable of novel mathematical thought either, but that doesn't mean they can't think.

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omnicognate 4 days ago | parent [-]

We don't understand individual neurons either. There is no level on which we understand the brain in the way we very much do understand LLMs. And as much as people like to handwave about how mysterious the weights are we actually perfectly understand both how the weights arise and how they result in the model's outputs. As I mentioned in [1] what we can't do is "explain" individual behaviours with simple stories that omit unnecessary details, but that's just about desiring better (or more convenient/useful) explanations than the utterly complete one we already have.

As for most humans not being mathematicians, it's entirely irrelevant. I gave an example of something that so far LLMs have not shown an ability to do. It's chosen to be something that can be clearly pointed to and for which any change in the status quo should be obvious if/when it happens. Naturally I think that the mechanism humans use to do this is fundamental to other aspects of their behaviour. The fact that only a tiny subset of humans are able to apply it in this particular specialised way changes nothing. I have no idea what you mean by "goalpost-shifting" in this context.

	▲	riku_iki 3 days ago \| parent \| next [-]
		> And as much as people like to handwave about how mysterious the weights are we actually perfectly understand both how the weights arise and how they result in the model's outputs we understand on this low level, but LLMs through the training converge to something larger than weights, there is a structure of these weights which emerged and allow to perform functions, and this part we do not understand, we just observe it as a black box, and experimenting on the level: we put this kind of input to black box and receive this kind of output.
	▲	int_19h 4 days ago \| parent \| prev [-]
		> We actually perfectly understand both how the weights arise and how they result in the model's outputs If we knew that, we wouldn't need LLMs; we could just hardcode the same logic that is encoded in those neural nets directly and far more efficiently. But we don't actually know what the weights do beyond very broad strokes.