I am agreement with the author that Chomsky is overly negative about LLMs, but I don't think this is a strong refutation (admittedly I've only spent 20 minutes skimming the article). LLMs give us a lot of insight into how computers/algorithms can manipulate and understand language, but that still does not tell me how humans do it (I am open to the possibility that this is how humans do it, but that evidence is not presented here).
One of Chomsky's main arguments is the poverty of stimulus (children seem to learn language with relatively little input). Here is what the author has to say:
> Large language models essentially lay this issue [poverty of stimulus] to rest because they come with none of the constraints. Modern language models refute Chomsky’s approach to language that others have insisted are necessary, yet they capture almost all key phenomena. It will be important to see, however, how well they can do on human-sized datasets, but their ability to generalize to sentences out-side of their training set is auspicious for empiricism.
That doesn't look like a refutation to me yet. We still need to do that test, but that still just tells us how you can do it algorithmically.
One of Chomsky's main arguments is the poverty of stimulus (children seem to learn language with relatively little input). Here is what the author has to say:
> Large language models essentially lay this issue [poverty of stimulus] to rest because they come with none of the constraints. Modern language models refute Chomsky’s approach to language that others have insisted are necessary, yet they capture almost all key phenomena. It will be important to see, however, how well they can do on human-sized datasets, but their ability to generalize to sentences out-side of their training set is auspicious for empiricism.
That doesn't look like a refutation to me yet. We still need to do that test, but that still just tells us how you can do it algorithmically.