[panarky]

I'm sorry, but the whole stochastic parrot thing is so thoroughly debunked at this point that we should stop repeating it as if it's some kind of rare wisdom.

AlphaGo showed that even pre-LLM models could generate brand new approaches to winning a game that human experts had never seen before, and didn't exist in any training material.

With a little thought and experimentation, it's pretty easy to show that LLMs can reason about concepts that do not exist in its training corpus.

You could invent a tiny DSL with brand-new, never-seen-before tokens, give two worked examples, then ask it to evaluate a gnarlier expression. If it solves it, it inferred and executed rules you just made up for the first time.

Or you could drop in docs for a new, never-seen-before API and ask it to decide when and why to call which tool, run the calls, and revise after errors. If it composes a working plan and improves from feedback, that’s reasoning about procedures that weren’t in the corpus.

[phs318u]

> even the pre-LLM models

You're implicitly disparaging non-LLM models at the same time as implying that LLMs are an evolution of the state of the art (in machine learning). Assuming AGI is the target (and it's not clear if we can even define it yet), LLM's or something like them, will be but one aspect. Using the example AlphaGo to laud the abilities and potential of LLM's is not warranted. They are different.

[suddenlybananas]

>AlphaGo showed that even pre-LLM models could generate brand new approaches to winning a game that human experts had never seen before, and didn't exist in any training material.

AlphaGo is an entirely different kind of algorithm.

[intended]

To build on the stochastic parrots bit -

Parrots hear parts of the sound forms we don’t.

If they riffed in the KHz we can’t hear, it would be novel, but it would not be stuff we didn’t train them on.