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Earlier quoted context omitted.
Wordcels think LLMs imitate the human brain, when a shape rotator knows they really just imitate human language.
doesn't this make LLM a dead end towards AGI and mostly just a neat specific trick?
(I’m agreeing with you basically)
Earlier quoted context omitted.
> The fact that you can replicate coherent text from probabilistic analysis and modeling of a very large corpus does not mean that humans acquire and generate language the same way. Also, the LLMs are cheating! They learned from us. It's entirely possible that you do need syntax/semantics/sapience to create the original corpus, but not to duplicate it. Let's see an AlphaZero-style version of an LLM, that learns langu…
>Also, the LLMs are cheating! No...they aren't. Humans aren't learning from thin air by any stretch of the imagination.
While I think some of the points in the article are interesting, the usual evidence for the Chomskian approach is the relative lack of input data for learning language by children in the wild. How much input data is used to train modern language models?
1. No Language model is yet even close to the scale of the human brain 2. Depending on what exactly you're trying to teach (perfect grammar, paragraphs of coherent text, basic reasoning), much less data is needed. https://arxiv.org/abs/2305.07759 3. Brains don't start at 0. Evolution, dna/rna etc. There's obviously some pre disposition for language learning in humans but that alone isn't enough ground for a "universa…
The author (bafflingly) seems to have completely missed the point- since anything they state up to page 15 (at which point I stopped reading) does not refute Chomsky's points at all. The author talks about LLMs and how they generate text and then goes on to talk about how it refutes Chomsky's claim about syntax and semantics. However it does not since Chomsky's primary claim is about how HUMANS acquire language. The…
Wordcels think LLMs imitate the human brain, when a shape rotator knows they really just imitate human language.
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.
The author seems to be stating that because llms have generated "interesting" statements that all of Chomsky's approaches are invalidated. This seems to assume that the llm isn't somehow incorporating Chomsky's generative grammar system into the model, which would be a very hard thing to prove.
Also, the author's stated proof contains flaws. Take for example page 16:
Generate ten other sentences like "colorless green ideas sleep furiously": Purple fluffy clouds dream wildly. Blue glittery unicorns jump excitedly.
The author claims that the llm has completed this task successfully, completely ignoring the example's pairwise incompatible terms (colorless green, and sleep furiously), and accepting that "purple fluffy" is equivalently meaningless. It is not, and the model has clearly failed.
The author (bafflingly) seems to have completely missed the point- since anything they state up to page 15 (at which point I stopped reading) does not refute Chomsky's points at all. The author talks about LLMs and how they generate text and then goes on to talk about how it refutes Chomsky's claim about syntax and semantics. However it does not since Chomsky's primary claim is about how HUMANS acquire language. The…
Biology is intrinsically local. For Chomsky’s model of language instinct to work, it would have to reduce down to some sort of embryonic developmental process consisting of entirely of local gene-activated steps over the years it takes for a human child to begin speaking grammatical sentences. This is in direct contrast to most examples of human instinct, which disappear very quickly as the brain develops.
Really the main advantage that Chomsky’s ideas had is that no one could imagine how something simpler could possibly result in linguistic understanding. But large language models demonstrate that no, actually one simple learning algorithm is perfectly sufficient. So why evoke something more complex?
> First, the fact that language models can be trained on large amounts of text data and can generate human-like language without any explicit instruction on gram- mar or syntax suggests that language may not be as biologically determined as Chomsky has claimed. Instead, it suggests that language may be learned and developed through exposure to language and interactions with others.
I'm not a linguist nor a cognitive scientist, but this seems so problematic that I am not sure that I read it correctly. For example, how is the fact that language models "work" contradict the innateness of language in humans?
While I think some of the points in the article are interesting, the usual evidence for the Chomskian approach is the relative lack of input data for learning language by children in the wild. How much input data is used to train modern language models?
1. No Language model is yet even close to the scale of the human brain 2. Depending on what exactly you're trying to teach (perfect grammar, paragraphs of coherent text, basic reasoning), much less data is needed. https://arxiv.org/abs/2305.07759 3. Brains don't start at 0. Evolution, dna/rna etc. There's obviously some pre disposition for language learning in humans but that alone isn't enough ground for a "universa…
If GPT-4 has 100 trillion parameters, it has as many parameters as the human brain has synapses. Synapses are a lot simpler than parameters; they're digital. A single neuron needs many synapses, all of roughly equal weight, emitting many pulses over a short time in order to convey a single weighted value.
On top of that, you may have heard that the human brain does a lot of things besides writing. You subtract the motor cortex, the visual and limbic systems etc... a 100 trillion parameter model is unambiguously larger than the language processing portions of the human brain.
> Brains don't start at 0. Evolution, dna/rna etc. There's obviously some pre disposition for language learning in humans but that alone isn't enough ground for a "universal grammar"
The human genome is 24 gigabits long. It's negligibly small compared to a language model.