The companies aren’t changing anything. LLM outputs are just more random than people realize. Run the same prompt 10 times if you really want to know how well they can answer.
Counterpoint: What progress has generative linguistics made in the same amount of time that deep learning has been around? It sure doesn't seem to be working well.
Also, the racecar example is because of tokenization in LLMs - they don't actually see the raw letters of the text they read. It would be like me asking you to read this sentence in your head and then tell me which syllable would have the lowest pitch when spoken aloud. Maybe you could do it, but it would take effort because it doesn't align with the way you're interpreting the input.
>What progress has generative linguistics made in the same amount of time that deep learning has been around? It sure doesn't seem to be working well.
Working well for what? Generative linguistics has certainly made progress in the past couple of decades, but it's not trying to solve engineering problems. If you think that generative linguistics and deep learning models are somehow competitors, you've probably misunderstood the former.
I think this is greatly complicated by the fact that the human brain has been "pre-trained" (in the deep learning sense) by hundreds of millions of years of evolution.
A pre-trained LLM also can also learn new concepts from extremely few examples. Humans may still be much smarter but I think there's a lot of reason to believe that the mechanics are similar.
The poverty of the stimulus (POS) argument is that "evolutionary pre-training" in the form (recursive) grammar is fundamentally required and can not be inferred from the stimulus.
The argument is based on multiple questionable assumptions of Chomskian linguistics:
- Humans actually learn grammar in the Chomskian way
- Syntax is separate from semantics, so only language (utterances) can be learned from uttrances, and not e.g. what is seen in the environment
- At least in the Gold's formalization of the argument language is learned only from "positive examples", so e.g. the learner can't observe that some does not understand some utterance
One could argue for a (very) weak form of POS that there has to be some kind of "inductive bias" in the learning system, but this applies to all learning as shown by Kant. The inductive bias can be very generic.
It seems to be a persistent myth (possibly revived more recently due to Norvig?) that Chomsky's POS argument has some interesting connection to Gold's theorem. The two things have only a very loose logical connection (Gold's theorem is in no sense a formalization of any claim of Chomsky's), and Chomsky himself never based any of his arguments for innateness on Gold's theorem. Here is a secondary source making the same point (search for 'Gold'): https://stevenpinker.com/files/pinker/files/jcl_macwhinney_c...
The assumption that syntax is 'separate from semantics' also does not figure in any of Chomsky's POS arguments. Chomsky argued that syntax was separate from semantics only in the fairly uncontroversial sense that there are properly syntactic primitives (e.g. 'noun', 'chain', 'c-command') that do not reduce entirely to semantic or phonological notions. But even if that were untrue, it would not undermine POS arguments, which for the most part can be run without any specific assumptions about the syntax/semantics boundary. Indeed, semantic and conceptual knowledge provides an equally fertile source of POS problems.
Yeah, I don't necessarily buy the whole Chomskian program. I'm willing to be persuaded that the reason kids learn to speak despite their individual poverty of stimulus is that there was sufficient empirically experience stimulus over evolutionary time. The Chomskian grammar stuff seems way too Platonic to be a description of human neuroanatomy. But be that as it may, it's clear the stimulus it takes to train an LLM is orders of magnitude greater than the stimulus necessary to train an individual child, so children must have a different process for language acquisition.
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