RT @JeffYoshimi: I've been having a nice time talking to @denizcemonduygu about maps and graphs of philosophical discourse, and thought I'….

RT @MH_Christiansen: @CSL_Lab alumn @pcontrerask talks about how feedback is crucial for getting large language models to produce more huma….

RT @TrendsCognSci: Information density as a predictor of communication dynamics. Spotlight by Gary Lupyan (@glupyan), Pablo Contreras Kalle….

RT @ElmlingerSteven: How do infants learn to produce the consonant sounds of their ambient language? To find out, check out our CogSci proc….

it's me!.

RT @JeffYoshimi: I'm delighted to announce the publication of our free, open access book, "Horizons of Phenomenology", a collection of essa….

RT @JeffYoshimi: Just realized I didn't include the link. Here it is!

RT @Benambridge: In sum, the ideas that children (a) have innate syntactic categories and (b) NEED them because they can't construct them v….

RT @Benambridge: I've seen lots of threads about large language models (LLMs) and their implications for language acquisition BUT not many….

RT @ross_dkm: For the Friday evening crowd still mulling over Chomsky’s op-ed, here’s a key passage from our recent letter in @cogsci_soc.….

RT @MH_Christiansen: @pcontrerask @ross_dkm And for those who’d like a less academic take on the issues in this article, take a look at our….

RT @raphaelmilliere: Another day, another opinion essay about ChatGPT in the @nytimes. This time, Noam Chomsky and colleagues weigh in on t….

So. Let's grant that GPT and its ilk are unreliable ethically problematic parrots. My hunch is that 20th Century theories of language would have been very different if parrots were half as good as GPT at summarizing the plot of Dragon Ball in the style of a Shakespearean sonnet.

Moreover, language has culturally evolved to fit the cognitive niche of humans, not LLMs. It is immediately assumed that the situation of a child is more precarious than an the LLM's. But this is not as obvious as a first hunch might tell you. It is, again, an empirical question.

But in another sense, this is a misleading characterization. Sure, LLMs get much more (although not INFINITELY more) words than a child. But a child is embedded in a natural and social world, interacts with it, and has a much richer experience and cognitive machinery than LLMs.

There's also the point of the amount of training and size of LLMs. More work needs to be done on how brittle this performance is, how much their similarities with humans are dependent on their size and gigantic input, or whether they can simulate developmental trajectories [. ].

There's a couple of nuances. How good of a model of human language learning LLMs are is still to be fully determined. However, this is an empirical question, and a lot of work keeps showing that the similarities are there. They can't be just waived away based on a hunch.

Replies that appeal to e.g. competence v/s performance, language being more than tracking statistics, etc., are assuming what they are supposed to show. This whole theoretical and conceptual apparatus was postulated because what these models can do was supposed to be impossible.

A historical example. In what is taken as a "seminal" takedown of connectionism, Pinker and Prince (1988) contemplate the possibility that NNs could "master the past tense", but that was merely a "vague hope" (183). Well, here we are, with a model that can do much more than that.

This contradicts the central core of the PoS. It is a little bit jarring to read people so casually dismissing something they claimed was literally impossible, i.e. a full-fledged, statistical learning-based model of grammar.