Restricted to linguistics, LLM's supposed lack of understanding should be a non-sequitur. If the question is whether LLMs have formed a coherent ability to parse human languages, the answer is obviously yes. In fact not just human languages, as seen with multimodality the same transformer architecture seems to work well to model and generate anything with inherent structure.
I'm surprised that he doesn't mention "universal grammar" once in that essay. Maybe it so happens that humans do have some innate "universal grammar" wired in by instinct but it's clearly not _necessary_ to be able to parse things. You don't need to set up some explicit language rules or generative structure, enough data and the model learns to produce it. I wonder if anyone has gone back and tried to see if you can extract out some explicit generative rules from the learned representation though.
Since the "universal grammar" hypothesis isn't really falsifiable, at best you can hope for some generalized equivalent that's isomorphic to the platonic representation hypothesis and claim that all human language is aligned in some given latent representation, and that our brains have been optimized to be able to work in this subspace. That's at least a testable assumption, by trying to reverse engineer the geometry of the space LLMs have learned.
I'm surprised that he doesn't mention "universal grammar" once in that essay. Maybe it so happens that humans do have some innate "universal grammar" wired in by instinct but it's clearly not _necessary_ to be able to parse things. You don't need to set up some explicit language rules or generative structure, enough data and the model learns to produce it. I wonder if anyone has gone back and tried to see if you can extract out some explicit generative rules from the learned representation though.
Since the "universal grammar" hypothesis isn't really falsifiable, at best you can hope for some generalized equivalent that's isomorphic to the platonic representation hypothesis and claim that all human language is aligned in some given latent representation, and that our brains have been optimized to be able to work in this subspace. That's at least a testable assumption, by trying to reverse engineer the geometry of the space LLMs have learned.