It's clear from this comment that you did not read the full article. If you did then you'd have seen that the author addresses this criticism you're making here.
It's clear from this comment that you did not read the full article. If you did then you'd have seen that the author addresses this criticism you're making here.
I did read it. She doesn’t mention mathematics or RLVR training once, so I assume you’re referring to my point about empirical testability. Well, I think her statement that the claim “LLMs are stochastic parrots” is not an empirical claim is false, and she’s being disingenuous there with a classic motte-and-bailey fallacy. She quotes her own original paper thus:
> Text generated by an LM is not grounded in communicative intent, any model of the world, or any model of the reader’s state of mind. It can’t have been, because the training data never included sharing thoughts with a listener, nor does the machine have the ability to do that. This can seem counter-intuitive given the increasingly fluent qualities of automatically generated text, but we have to account for the fact that our perception of natural language text, regardless of how it was generated, is mediated by our own linguistic competence and our predisposition to interpret communicative acts as conveying coherent meaning and intent, whether or not they do [89, 140]. The problem is, if one side of the communication does not have meaning, then the comprehension of the implicit meaning is an illusion arising from our singular human understanding of language (independent of the model). Contrary to how it may seem when we observe its output, an LM is a system for haphazardly stitching together sequences of linguistic forms it has observed in its vast training data, according to probabilistic information about how they combine, but without any reference to meaning: a stochastic parrot.
Do you really think that claiming the output of an LLM “has no reference to meaning” is not an empirical claim? That it doesn’t attempt to place any bounds whatsoever on what LLMs can and cannot do? LLMs can solve some very difficult mathematical problems quite well now: see the article from Gowers that was on here recently. Do you think that the output in a situation like that “has no reference to meaning?” If so, you’ll have to explain why, because I don’t understand at all.