[e12e]

I think there are quite fundamental differences to the LLM model equivalent to an adult human mind and current LLMs (even accepting an LLM can model a human brain, which I don't think is a given - but I also don't think it's impossible).

The details matter (training set, full context). I doubt our current LLMs knows what smelling cut grass on a wet morning feels like, or how the stomach flutters on a first kiss - even if they have "read" tens to hundreds of attempts at describing such things.

You're essentially suggesting we play Turing's Guessing Game; if your LLM can guess my response to any prompt - then it can be said to have modeled how I would reply to those prompts. Expand the prompts far enough, and you could reasonably argue the LLM models me.

[bob001]

>The details matter (training set, full context).

But you said:

>Training shifts the probability - but doesn't change the fact that the output is a sampling based on input and the model?

So which is it?

>You're essentially suggesting we play Turing's Guessing Game; if your LLM can guess my response to any prompt - then it can be said to have modeled how I would reply to those prompts. Expand the prompts far enough, and you could reasonably argue the LLM models me.

I'm merely noting that your argument that "training+input=>output is the problem" applies to humans. There's other arguments for and against LLMs however I'm simply pointing out that your argument isn't a good one.

[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.