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You aren't contradicting the person.
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They definitely are - the OP claimed that we are reaching "the end of the road for LLMs", based on absolutely no data and some handwaving on pareto distribution.

We absolutely don't know enough about LLMs and intelligence to make such a bold (and ridiculous) claim. If anything, all evidence point to the contrary, with new scientific breakthrough achieved across a variety of fields via LLMs.

I've been really struggling to understand how the HN community can so boldly claim that LLMs are going to stop improving or not really smart. I just read it as the "denial" stage of the stages of grief that a good portion of this community is in right now (which is understandable).

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We know plenty about human cognition, and we know everything about how LLMs work. True we don’t know anything about intelligence but that is because “intelligence” is it self a fraught and vague term, and we haven’t (and perhaps never will) settled on what it means exactly.
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I'm sorry, we know exactly how LLMs work, this myth that we "dont know how they work" was perpetuated by executives that dont know how they work.

We know exactly how attention layers work and how they produce the next word as well as draw them from larger feature spaces.

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Right, but we have no clue why, and how the emergent behavior they show works.

If we would know that, there would be no need for interpretability research.

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If you know all this, can you explain how these models produce advanced mathematical proofs? (as recently done by OpenAI, for example)

I tried to generate the next word to the best of my ability, starting with a mathematical problem, but I did not create a valid proof. How do these LLMs work when they create math proofs to problems not yet solved?

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Yes, it turns out that matrix math over a feature space of math works pretty well because unlike poetry or real world work, maths are internally coherent and entirely theoretical.
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This not understanding "understanding".

> maths are internally coherent and entirely theoretical

Nope. This kind of wish-washy thinking is not what we mean by understanding.

https://iep.utm.edu/math-inc/

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How did you generate the next word? Did you first read pretty much every written work ever published, including blog posts, forum posts, books, etc? Learn how to imagine everything as a point in a gigantic abstract space where similar meanings cluster together? How did you manage training with gradient descent? And then did you do a lifetime of matrix multiplication for each token you predicted?
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You can see how an LLM works here https://bbycroft.net/llm they are not magic.
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You don’t have the computational ability to process as many calculations as a datacenter. You can hardly transpose a 5×5 matrix in your mind, so you won’t be able to do what datacenters do.

This is like saying we don‘t know how a car works because a car can beat the best human athletes in 100 meter dash.

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