8 ms·

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

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.

lalos 4y ago
Not relevant but: "Every time I fire a linguist, the performance of the speech recognizer goes up". - Frederick Jelinek
- jojobas 4y ago
  Surely the remaining linguists muster up some improvements out of fear for their jobs!
PeterisP 4y ago
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 http://incompleteideas.net/IncIdeas/BitterLesson.html) which we don't want to accept.
- deleted 4y ago
  [deleted]
- clementneo 4y ago
  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 https://twitter.com/janleike/status/1625207251630960640
- kilgnad 4y ago
  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.
  - vajrabum 4y ago
    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.
    - kilgnad 4y ago
      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?
KhoomeiK 4y ago
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://arxiv.org/abs/1905.05950 https://aclanthology.org/N19-1419/ https://aclanthology.org/N19-1419/ https://arxiv.org/abs/1906.04341 https://arxiv.org/abs/1906.04341
- compressedgas 4y ago
  I would add https://www.pnas.org/doi/10.1073/pnas.1907367117 https://www.pnas.org/doi/10.1073/pnas.1907367117
  - KhoomeiK 4y ago
    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.
carabiner 4y ago
It's so weird. And statistical weather models are doing better than the physical ones, at far lower computational cost.
Baeocystin 4y ago
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.
- solarmist 4y ago
  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.
- letmevoteplease 4y ago
  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 https://en.wikipedia.org/wiki/Input_hypothesis
  - nmfisher 4y ago
    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.
    - charcircuit 4y ago
      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.
dgut 4y ago
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.
- csb6 4y ago
  > 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.
- IIAOPSW 4y ago
  That is not what universal grammar was about. Literally read even the wikipedia page or watch like any lecture on the topic.
  - dgut 4y ago
    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.
    - IIAOPSW 4y ago

lalos 4y ago

Not relevant but: "Every time I fire a linguist, the performance of the speech recognizer goes up". - Frederick Jelinek

jojobas 4y ago
Surely the remaining linguists muster up some improvements out of fear for their jobs!

PeterisP 4y ago

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 http://incompleteideas.net/IncIdeas/BitterLesson.html) which we don't want to accept.

deleted 4y ago
[deleted]
clementneo 4y ago
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 https://twitter.com/janleike/status/1625207251630960640
kilgnad 4y ago
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.
- vajrabum 4y ago
  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.
  - kilgnad 4y ago
    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?

KhoomeiK 4y ago

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://arxiv.org/abs/1905.05950 https://aclanthology.org/N19-1419/ https://aclanthology.org/N19-1419/ https://arxiv.org/abs/1906.04341 https://arxiv.org/abs/1906.04341

compressedgas 4y ago
I would add https://www.pnas.org/doi/10.1073/pnas.1907367117 https://www.pnas.org/doi/10.1073/pnas.1907367117
- KhoomeiK 4y ago
  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.

carabiner 4y ago

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

Baeocystin 4y ago

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.

solarmist 4y ago
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.
letmevoteplease 4y ago
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 https://en.wikipedia.org/wiki/Input_hypothesis
- nmfisher 4y ago
  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.
  - charcircuit 4y ago
    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.

dgut 4y ago

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.

csb6 4y ago
> 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.
IIAOPSW 4y ago
That is not what universal grammar was about. Literally read even the wikipedia page or watch like any lecture on the topic.
- dgut 4y ago
  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.
  - IIAOPSW 4y ago