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The wall confronting large language models

(arxiv.org)
170 points PaulHoule | 1 comments | 03 Sep 25 11:40 UTC | HN request time: 0s | source
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measurablefunc ◴[03 Sep 25 20:29 UTC] No.45120049[source]▶
There is a formal extensional equivalence between Markov chains & LLMs but the only person who seems to be saying anything about this is Gary Marcus. He is constantly making the point that symbolic understanding can not be reduced to a probabilistic computation regardless of how large the graph gets it will still be missing basic stuff like backtracking (which is available in programming languages like Prolog). I think that Gary is right on basically all counts. Probabilistic generative models are fun but no amount of probabilistic sequence generation can be a substitute for logical reasoning.
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1. Straw ◴[04 Sep 25 02:07 UTC] No.45122702[source]▶
This is utter nonsense.

There's a formal equivalence between Markov chains and literally any system. The entire world can be viewed as a Markov chain. This doesn't tell you anything of interest, just that if you expand state without bound you eventually get the Markov property.

Why can't an LLM do backtracking? Not only within its multiple layers but across token models as reasoning models already do.

You are a probabilistic generative model (If you object, all of quantum mechanics is). I guess that means you can't do any reasoning!