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Comment by LudwigNagasena | Hacker News Reader

It’s notable how successful LLMs despite the lack of any linguistic tools in their architectures. It would be interesting to know how different a model would be if it operated on eg dependency trees instead of the linear list of tokens. Surely, the question of “a/an” would be solved with ease as the model would be required to come up with a noun token before choosing its determiner. I wonder if the developers of LLMs explored those approaches but found them infeasible due to large preprocessing times, immaturity of such tools and/or little benefit.

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I think the lack of explicit linguistic tools is the key to success, forcing/enabling the generic model to learn implicit linguistic tools (there's some research identifying that analysis of specific linguistic phenomena happens at specific places in the NN layers) that work better than what we could implement.

"It would be interesting to know how different a model would be if it operated on eg dependency trees instead of the linear list of tokens." - indeed, this is obviously interesting, so people have tried that a lot for many models, but IMHO it's probably now almost decade since the consensus is that in general end-to-end training (once we became able to do it) work better than adding explicit stages in between, e.g. for any random task I would expect that doing text->syntax tree->outcome is going to get worse results than text->outcome, because even if the task really needs syntax, the syntax representation that a stack of large transformer layers learns implicitly tends to be a better representation of the natural language than any human linguist devised formal grammar, which inevitably has to mangle the actual languge to fit into neat human-analyzable 'boxes'/classes/types in which it doesn't really fit and all the fuzzy edge cases stick out. Once you remove the constraint that the grammar must be simplified enough for a human to be able to rationally analyze and understand, and (perhaps even more importantly?) abandoning the need to prematurely disambiguate the utterance to a single syntax tree instead of accepting that parts of it are ambiguous (but not equally likely), processing works better.

It's just one more reminder of the bitter lesson (http://incompleteideas.net/IncIdeas/BitterLesson.html) which we don't want to accept.

I think there's probably some truth to this. They found that in InstructGPT — where they teach the model to better follow instructions, which was the jump from GPT-3 to ChatGPT — they found that the model also learnt to follow non-English instructions, even though the extra training was done almost exclusively in English[1].

So there seems to be such emergent mechanisms in the model that have arisen because of the end-to-end training, which we don't exactly understand yet.

[1] https://twitter.com/janleike/status/1625207251630960640

Hmm I think it's sort of a hybrid.

First off, we know that overall the concept of language is humans is an emergent phenomenon. It developed from natural selection from simple components so there's validity that the same thing can occur in an LLM where some overarching emergent structure develops from simple primitives.

At the same time we do know that a sort of universal grammar exists among humans. Our language capacity is biased in a certain way and that it is unlikely for it to learn languages of a very extreme and divergent grammar from the universal one discovered by Noam chomsky. That means our brain is unlikely to be as universally simple as an LLM.

I think the key here is that the human mind has explicit linguistic tools but the these tools are still emergent in nature.

Lots of linguistics researchers would disagree about the existence and necessity of universal grammar or Chomsky's 'language acquisition device' and in fact the success of LLMs and statistical models which very clearly have no LAD and no universal grammar over feature engineering or grammar based schemes suggests the opposite.

I'm outdated on this research. I took a linguistics class in UCLA about a decade ago and at the time universal grammar was the prevailing norm.

Has it changed since then? Have they found a significant number of human civilizations that use divergent grammars?

Chomsky’s universal grammar is a modification of Harris’s Operator grammar.

Harris’s operator grammar was based on set theory however Chomsky was enamored with formal logic and ran in that direction. Also, Chomsky became famous while Harris didn’t.

Operator grammar is self-discoverable and he published a full description of English grammar in the 1980’s using this theory and an extension in the 1990’s which generalized to other languages.

It is not fully deterministic (final word selection and ordering is probabilistic), but it is much more convincing to me and in line with how we understand brains to work.

Over the last 20 years Chomsky has begun to fall out of favor because it’s just so complicated and requires external structures, etc.

Well the model needs to be invalidated by science not by elegance or logic. Which grammar is actually more universal or is it a case where both can be applied. In the class I attended Chmosky's stuff was taught as foundational linguistics.

I’m not sure what you’re arguing.

Any theory that explains all the facts is valid, there can be more than one competing theory. Fame of the author absolutely matters and so does elegance of the theory.

Chomsky has held on so long because it did pretty well and he became very famous, but over time it has needed larger and larger patches to cover its flaws, so other explanations are gaining traction once again.

Hold on. I'm not biased here. I already admitted my knowledge is outdated. I am actively welcoming new information. I am not arguing.

No the elegance of the theory does not matter. The truth of the theory does. I'm saying does the universal grammar you describe, is it actually universal? Amongst all the languages in the world, do they all share this other grammar you describe? If not then the theory is incorrect. It doesn't matter how elegant the theory is.

I brought this up specifically because your reasoning wasn't "scientific" it was more along the lines of logical elegance.

>Chomsky has held on so long because it did pretty well and he became very famous, but over time it has needed larger and larger patches to cover its flaws, so other explanations are gaining traction once again.

See this makes sense and is basically the answer to the question I am asking. So you're basically saying that there were flaws? As in he had to make up new rules constantly because the science was contradicting his theory. Am I correct in this characterization?

Ah, gotcha. Yeah, I didn’t really get what you were trying to say.

I’m not an expert, but if I recall I think the big flaw was insistence on determinism.

And to my knowledge operator grammar is universal to the degree there isn’t a counter example in the dozen of languages he explored and hundreds he had others help him with. But to my knowledge the only language he fully mapped was English.

Edit: take a look at the article on Wikipedia.

https://en.m.wikipedia.org/wiki/Operator_grammar

Not relevant but:

"Every time I fire a linguist, the performance of the speech recognizer goes up". - Frederick Jelinek

Surely the remaining linguists muster up some improvements out of fear for their jobs!

Approaches such as you describe have been the dominant method for decades. That we finally 'cracked' natural language generation with tools that literally encode nothing about grammar ahead of time is one hell of a lesson, early days as it is in the learning of it.

Reminds me of Stephen Krashen's input hypothesis of second-language acquisition. Krashen argues that consciously studying grammar is more or less useless, and only massive exposure to the language results in acquisition.[1] This is true in my experience.

[1] https://en.wikipedia.org/wiki/Input_hypothesis

I'm designing a Chinese learning app and I'd mostly agree with this. My working hypothesis though? Most adult learners don't have the time/patience for massive/lengthy exposure, so grammar lessons are a shortcut to "feeling" like they're making progress.

I think it's a mistake to discount the psychology of learning. It's like saying that "calories-in, calories-out" is all there is to weight-loss. Strictly true, but not helpful for 90% of people.

Grammar lessons are a shortcut. So are books and other corpuses of knowledge. People have spent time documenting patterns that exist and its useful to learn from them instead having to brute force everything yourself.

This is assuming that the brain regions that learn rules overlap with the brain regions that develops fluency in a language. I think Krashen's hypothesis is that this is largely not the case. You can "fake" some degree of competence by learning the rules and using that brain region, but you're slow and not fluent until you expose the other region to enough real-world data.

Ref, A Bitter Lesson

Harris, Chomsky’s advisor, published his operator grammar over the decades and one of the key features was that it is completely self-discoverable.

Probability and observation are all that is required to understand a language.

Several papers have explored emergent linguistic structure in LMs. Here's some early introductory work in this space. Despite having explicit syntax parses etc as input, models seem to learn something like syntax.

https://arxiv.org/abs/1905.05950

https://aclanthology.org/N19-1419/

https://arxiv.org/abs/1906.04341

I would add https://www.pnas.org/doi/10.1073/pnas.1907367117

Yeah this is more or less a re-release of the second work I linked, except for a broader audience and more targeted at linguistics / cognitive science researchers.

grammar as we know it was devised for the Latin language and linguists spend most of the time attempting to fit other languages into neat boxes that the Latin grammar wasn't designed for. This of course leads to absurdity. Chomsky attempted to solve this problem with his universal grammar, but that too stops working quickly once you get outside of European languages. That is, ignoring linguistic tools is one of the reasons GPT is successful.

> that too stops working quickly once you get outside of European languages

I don’t think this is entirely fair. Generative grammars have been produced for a huge variety of non-European languages, even non-Indo-European languages, and can account for tremendous diversity in linguistic rules. Even languages without fixed word orders or highly synthetic languages can be represented.

Linguistics isn’t focused on the problem of outputting reasonable-sounding text responses. Instead, it seeks to transparently explain how language works and is structured, something that GPT does not do.

That is not what universal grammar was about. Literally read even the wikipedia page or watch like any lecture on the topic.

What exactly did I write that is wrong? UG argues a universal grammar exists, and that humans innately posses knowledge about this grammar. It's this grammar that enables humans to learn a language (according to UG). They (UG linguists) have created a system of syntax rules that attempts to describe any language, but failed at doing so once they step outside of Indo-European languages. This is partly because Chomsky was hired by MIT to solve machine translation and putting language into a set of neat boxes was his best idea and partly because Chomsky himself had little knowledge of other languages. It's pseudoscience.

Ok now you've made it mostly accurate credit where its due. This is not what you first said.

>grammar as we know it was devised for the Latin language and linguists spend most of the time attempting to fit other languages into neat boxes that the Latin grammar wasn't designed for....Chomsky tried to . All solve this problem with Universal Grammar.

Well first of all, every language has some form of grammar. If there were no rules that made meaning depend on word order, there would be nothing different between what the cat ate and what ate the cat. To claim Grammar is some Latin/Western system being unduly applied is absurd.

You are correct UG was invented as a to explain something about English. Howevert was not an attempt to solve "this problem ("this problem" being conforming to expected systems of Grmmar.) The problem he was interested in solving was about the ability to learn language rapidly despite not having many negative examples of how not to talk. Pay attention here researchers, the lesson maybe extends to you too soon.

UG also isn't a specific enough thing you can check against in a directory. Its a theory, namely a theory that if you study language features you'll eventually discver some things are invaiant. Using it directly and immediately based on nothing but Chomsky's conjecture...all I can focus on passing out at screen goodnightn

It's so weird. And statistical weather models are doing better than the physical ones, at far lower computational cost.

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