Here is a slightly sideways take on the question. Since some misunderstandings crop up over and over.

Things that are not true about neural networks and LLMs:

1. They are not stochastic parrots. Statistics can be used to judge model performances, as with any type of model whatsoever.

But that doesn’t make a model statistical. Neural network components are not statistically driven, they are gradient driven. They don’t calculate statistics, they learn topological representations of relationships.

2. LLMs are not just doing word prediction or text completion. Yes, that is the basic task they are trained on, but the “just” (that is often stated or implied) trivializes what the network actually has to learn to perform well.

Task type, and what must be learned to achieve success at that task, are two entirely different things.

To predict the kinds of human reasoning documented in writing in training sets, requires that kind of reasoning be learned. Not just some compressed generalization of people’s particular responses.

Simple proof that LLMs are not just compressing a lot of human behavior comes easily. Just ask an LLM to do something involving several topics unlikely to have been encountered together before, and their one-shot answer might not be God’s word on the issue, but is a far cry from the stumbling that good mimic could ever do. (Example task, ask for a Supreme Court brief to argue for rabbits rights based on sentient animal and native rights, with serious arguments, but written in Dr. Seuss prose by James Bond.)

3. LLMs do reason. Not like us or as well as us. But also, actually better than us in some ways.

LLMs are far superior to us at very wide, somewhat shallow reasoning.

They are able to one-shot weave together information from tens of thousands of disparate topics and ideas, on demand.

But they don’t notice their own blind spots. They miss implications. Those are things humans do quickly in continuous narrower deeper reasoning cycles.

They are getting better.

And some of their failures should be attributed to the strict constraint of one/few-shot(s) to well written responses, that we put on them.

We don’t hold humans to such strict standards. And if we did, we would also make a lot more obvious errors.

Wide & shallow reasoning in ways humans can’t match, is not a trivial success.

4. LLMs are very creative.

As with their reasoning, not like us: very wide in terms of instantly and fluidly weaving highly disparate information together. Somewhat shallow (but gaining) in terms of iteratively recognizing and self-correcting their own mistakes and oversights.

See random original disparate topic task above.

First, spend several hours performing that task oneself. Or take a day or two if need be.

Then, give the task to an LLM and wait a few seconds.

Compare. Ouch.

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TLDR; somewhat off topic and high level, but when trying to understand models it helps to avoid seemingly endlessly repeated misunderstandings.

“LLMs are far superior to us at very wide, somewhat shallow reasoning.”

So how much will it cost to autocomplete Wikipedia?

i hate "stochastic parrot" because it's not even really meaningful

I think it's true that models are statistical, inasmuch as P(A|B) where B is the prior sequence is what the loss is computing, and that's statistical. It's just computing that function in an absurdly complex way, which involves creating topological representations of relationships, etc.

I agree that "just" autocomplete implies the wrong thing. It turns out autocomplete is amazing if you scale it up.

I think it's true that they reason and area creative but these are really hard points because people mean subtly different things when saying "reason" and "creative".