Comment by e12e

> I'm merely noting that your argument that "training+input=>output is the problem" applies to humans.

I'm not sure I ever said that it was a problem.

I also never meant to say that sampling across the model (formed by training) based on input couldn't produce novel token sequences.

As for human vs LLM - my argument would be that while trained on a large corpus, the models are poor in experience and "sensory input" - their training so different from growing up - that even if we could model humans as LLMs - the gap between current LLMs and "human" LLMs remain a wide one.

More to the point - I believe the current generation of LLMs are way too sensitive to input (prompt, system prompt, context/RAG) - precisely because they're still too narrowly trained, and also aligned to weigh input heavily.

What we have today are models that work well with language (generate, transform, translate) - not so great on complex tasks - because they're still generating next probable token.