It's also a specific choice of label though, and one which in the context of current discourse favors a particular side.

It's like insisting on calling anything physical "atom collections". Yes, we get it, it's true (under a certain interpretation)—but it's clearly pointless to say except as an attempt at devaluing through reduction. (And it takes a particular stance on what it means to "be" something: to say it's literally the truth that anything physical is "just atoms" isn't the only way of looking at it.)

There were things we could've called "statistical word generators" decades ago; insisting on using a term directed at that level of generality implies a belief that nothing significant has happened since. Printing press? Just atoms. Cars? Just atoms. Computers? Just atoms.

I think its because it's sort of gone in the opposite way that science usually goes. Science traditionally been the process of physical phenomena that we don't understand => falsifiable theory that helps describe that phenomena.

Meanwhile, we started at stuff like the perceptron. The starting point was that we knew everything about that equation/classifier. Now we have a thing that we built from the ground up, and we don't fully grasp how it all comes together.

> insisting on using a term directed at that level of generality implies a belief that nothing significant has happened since. Printing press? Just atoms. Cars? Just atoms. Computers? Just atoms.

It's very context-dependent but I don't read this as dismissive reduction, I read it as a statement of the incredible generalisability of atoms under the current laws of physics.

Pfft more useless text written by a giant pile of atoms. Pointless.

Kidding aside, in the current context of human communication etiquette, to even refer to something as atoms outside of a demonstration or example is a deliberate dismissive reduction.

I see the current context as something narrower, namely a discussion on the technical underpinnings of LLMs.

The output of LLMs implies a higher level structure. You can't solely discuss the lower level underpinning unless you deliberately do not acknowledge that the output of LLMs are implying something else.

I think the fact that it's a technical discussion highlights why it's problematic: it might be appropriate for a total outsider who needs some kind of grasp of the basic underlying principle it operates on; but in discussion by a bunch of people with CS backgrounds, calling it merely a statical language generator erases distinction between it and other software of a prior age known not to be world-changing, overlooks recent innovations we don't yet have simple ways of referring to (largely because we don't understand emergent properties of LLMs)

Except the paper in the OPs comment literally talks about how these LLM's are building actual models of the real world. No need to be worried about knocking chatGPT. I have no pride invested in the thing. But I do think that people who view it solely as statistical word generators are biased.

You can imagine that the embedding for the token “boat” includes wood, steel, water and so forth in some set of probabilistic paths as learnt by autorgressive training since the words appear together in the past N tokens. So they are directly in frame. A question is how to connect out of frame elements and are overlapping tokens sufficient to do “that”. Specifically is the token “that” sufficiently trained to reveal what it refers to? I think this depends on the fine tuning q/a task which adds in a layer of alignment rather than being an emergent property of the LLM in general.

Still alignment tasks are autoregressive (I think)… they could be masked or masked part of speech potentially.. but if autoregressive then I suspect you’re looking at regularities in positioning to identify things.

We've spent years building tools that generate sequences with probable statistical distributions, but none of them ever developed anything that vaguely resembled a "world model" before. I think it might be useful to disambiguate these transformer models from previous statistical text generators, in the same way it's useful to disambiguate a human brain from a calculator (even if they're both trained to perform arithmetic.)