The contention that there is no grounding because the training data is linguistic and thus can only reference a world model is disproven in "This sentence has five words"- there's real, grounded information about what "five" means within that sentence. While that's a trivial counterexample, I don't know that it's an obvious one (I didn't come up with it myself).

It's not a criticism of the paper itself, but multimodal models came shortly after and provide grounding that is more of the sort the paper is getting at, and it didn't seem like anybody updated on that at all. If multimodal models were still stochastic parrots by the original argument, humans would have to be as well; we don't have any way to ground anything beneath sense data and evolution can't have programmed some innate grounding into us because it didn't either. But (and maybe this is my own misperception) nobody threw in the towel at that point.

I confess I never read the original paper until now, opting to absorb by osmosis instead, and I was quite surprised that they don't really make a deeper case than that. After just a few paragraphs about how they can't be grounded because humans don't express their thoughts directly, it lurches into a page about how they can be biased by training. And they certainly can be, but that has little to say about their stochastic nature- humans are biased as a rule with no exception. (For the record, I only read the Stochastic Parrots section before this reply.)

It's not really a bad paper, but I don't see why it ever carried the esteem it did. Hating on it is like hating on Taylor Swift- she's fine, yes, but for her level of success, one is inclined to question every dumb lyric where others get a pass. (Apologies to Swift fans, substitute a successful artist you don't care for here.)