3 ms·

I mean, contrary to Chomsky, that certainly advanced statistical models are able to capture all grammar - at least, nit worse than ordinary humans. It feels th

by stared 2y ago

I mean, contrary to Chomsky, that certainly advanced statistical models are able to capture all grammar - at least, nit worse than ordinary humans. It feels that Chomsky’s understanding of ML stopped at the level of Markov chains.

foldr 2y ago
Is there any work demonstrating this? For example, how do statistical models capture adjunct/argument asymmetries in extraction?
- stared 2y ago
  Attention is one of the core parts of transformer architecture, so I would be surprised if they have any trouble understanding this asymmetry. Could you provide a testable hypothesis? I would be happy to test it on GPT4.
  - foldr 2y ago
    Sure, here are a couple of examples of ECP violations removing ambiguities. 1a. How often did you tell John that he should take out the trash? b. How often did you tell John why he should take out the trash? (1a) can either be a question about frequency of telling or frequency of trash disposal, whereas (1b) can only be a question about frequency of telling. I asked GPT-4 to explain how each sentence was ambiguous and it seemed to entirely miss the embedded readings (the ones about frequency of trash disposal) for both sentences, while finding some other ambiguities that were spurious (such as suggesting erroneously that (1b) could be a question about how many different reasons you gave John in a single instance). Similarly, (2a) has both a de re and a de dicto reading, whereas (2b) has only a de re reading: 2a. How many books did Bill say that Mary should read? b. How many books did Bill explain why Mary should read? That is, (2a) can be asked either in a scenario where Bill has said "read 10 books!" or in a scenario where Bill has said "read Book A, Book B and Book C!" without necessarily counting the books himself. (2b), on the other hand, only has the second kind of interpretation. I've had mixed results with GPT-4 in this case (depending on exact choices of vocabulary, etc.), but it certainly makes some mistakes. For example, it says that (2b) can mean "John explained the reason for a certain number of books that Mary bought". As the sibling comment points out, it would not show very much if GTP-4 did correctly determine these ambiguities as it has had access to much more data than a child. You would also need to show that the same statistical techniques would work when applied to a realistic dataset.
    - stared 2y ago
      Thank you for providing these examples. I asked GPT4o, and it has no trouble with understanding 1a: https://chatgpt.com/share/daea469f-d823-45e2-9d6b-f6bea82a2623 https://chatgpt.com/share/daea469f-d823-45e2-9d6b-f6bea82a26... As a side note, my instinctive reading is on the telling frequency. Sure, one can make a garden path sentence, but (for my own ESL eyes and ears) it would be more straightforward to say, "Did you tell John how often he should take out the trash?" 2b does not feel right on its own (and I am not an AI). I can understand it, but it feels like reverse engineering rather than reading a normal sentence.
      - foldr 2y ago
        The whole issue is the difference between (1a) and (1b), not whether the AI can understand one of the sentences under some of its available interpretations. Indeed, with GPT-4o, I get the same result as you for (1a), but also a description of a spurious parallel ambiguity in (1b). Part of the trouble here is the inconsistency of results depending on the exact phrasing of the question and random variation in GPT-4's responses. I wouldn't be surprised if it sometimes gives the right answers, but I don't think it does so reliably. (2b) is what's known in classical terms as a 'subjacency violation', so yes, it sounds imperfect. Nonetheless, native speakers agree on which interpretations it can and cannot have. GPT-4 does not have the same capacity, as far as I've been able to determine. You sometimes have to be a little creative with scenario construction for sentences like (2b) to click. "Ok, So Bill explained why Mary should read War and Peace, then he explained why she should read The 39 Steps, and then he explained why she should read some other titles that I can't remember. I wonder just how many books Bill explained why Mary should read." But try constructing a scenario for the other interpretation and you'll find that it's still just as bad.
        stared 2y ago
        Well, prompting is not a nice addition to LLM; it is a necessary thing to do. > Nonetheless, native speakers agree on which interpretations it can and cannot have. GPT-4 does not have the same capacity, as far as I've been able to determine. This one is an expectation, not even a factual statement. A factual statement would be "95% of English native speakers with a college degree" or so. Among less educated, the numbers could be depressingly low, even for much more straightforward tasks. Then, the question is how a given ML model fares against real data, not against some platonic ideal.
foobarqux 2y ago
First, Chomsky (and I) were talking about language acquisition in children where there aren't billions of examples, so it's completely irrelevant if some other system can do something, the question is how do humans do it. Second, there isn't any evidence that LLMs have captured grammar rules in any meaningful sense, just as they can't do addition or any other recursive computation.
numpad0 2y ago
Don't waste your time arguing Chomsky supporters, it's a cult. They keep wringing non-falsifiable theories out of lengthy hallucinations, but it all reveals itself to be just Trek physics later on. He's not just problematic in politics, he is in linguistics too, just the BS is harder to notice. I suppose his compiler theories were legit, and his pioneering spirit that lead to establishment of multiple fields of researches might be too, but leading theories he'd created are just as dubious as the first man made shed built on a discovered island would be. And the problem is not just that early speculations in a novel field are often wrong, it's that his supporters don't care. They'd regurgitate those scientific theories(tm) ad infinitum and waste resources for the whole humanity. So don't bother trying to fix them and making them motivated.

by stared 2y ago

foldr 2y ago

Is there any work demonstrating this? For example, how do statistical models capture adjunct/argument asymmetries in extraction?

stared 2y ago
Attention is one of the core parts of transformer architecture, so I would be surprised if they have any trouble understanding this asymmetry. Could you provide a testable hypothesis? I would be happy to test it on GPT4.
- foldr 2y ago
  Sure, here are a couple of examples of ECP violations removing ambiguities. 1a. How often did you tell John that he should take out the trash? b. How often did you tell John why he should take out the trash? (1a) can either be a question about frequency of telling or frequency of trash disposal, whereas (1b) can only be a question about frequency of telling. I asked GPT-4 to explain how each sentence was ambiguous and it seemed to entirely miss the embedded readings (the ones about frequency of trash disposal) for both sentences, while finding some other ambiguities that were spurious (such as suggesting erroneously that (1b) could be a question about how many different reasons you gave John in a single instance). Similarly, (2a) has both a de re and a de dicto reading, whereas (2b) has only a de re reading: 2a. How many books did Bill say that Mary should read? b. How many books did Bill explain why Mary should read? That is, (2a) can be asked either in a scenario where Bill has said "read 10 books!" or in a scenario where Bill has said "read Book A, Book B and Book C!" without necessarily counting the books himself. (2b), on the other hand, only has the second kind of interpretation. I've had mixed results with GPT-4 in this case (depending on exact choices of vocabulary, etc.), but it certainly makes some mistakes. For example, it says that (2b) can mean "John explained the reason for a certain number of books that Mary bought". As the sibling comment points out, it would not show very much if GTP-4 did correctly determine these ambiguities as it has had access to much more data than a child. You would also need to show that the same statistical techniques would work when applied to a realistic dataset.
  - stared 2y ago
    Thank you for providing these examples. I asked GPT4o, and it has no trouble with understanding 1a: https://chatgpt.com/share/daea469f-d823-45e2-9d6b-f6bea82a2623 https://chatgpt.com/share/daea469f-d823-45e2-9d6b-f6bea82a26... As a side note, my instinctive reading is on the telling frequency. Sure, one can make a garden path sentence, but (for my own ESL eyes and ears) it would be more straightforward to say, "Did you tell John how often he should take out the trash?" 2b does not feel right on its own (and I am not an AI). I can understand it, but it feels like reverse engineering rather than reading a normal sentence.
    - foldr 2y ago
      The whole issue is the difference between (1a) and (1b), not whether the AI can understand one of the sentences under some of its available interpretations. Indeed, with GPT-4o, I get the same result as you for (1a), but also a description of a spurious parallel ambiguity in (1b). Part of the trouble here is the inconsistency of results depending on the exact phrasing of the question and random variation in GPT-4's responses. I wouldn't be surprised if it sometimes gives the right answers, but I don't think it does so reliably. (2b) is what's known in classical terms as a 'subjacency violation', so yes, it sounds imperfect. Nonetheless, native speakers agree on which interpretations it can and cannot have. GPT-4 does not have the same capacity, as far as I've been able to determine. You sometimes have to be a little creative with scenario construction for sentences like (2b) to click. "Ok, So Bill explained why Mary should read War and Peace, then he explained why she should read The 39 Steps, and then he explained why she should read some other titles that I can't remember. I wonder just how many books Bill explained why Mary should read." But try constructing a scenario for the other interpretation and you'll find that it's still just as bad.
      - stared 2y ago
        Well, prompting is not a nice addition to LLM; it is a necessary thing to do. > Nonetheless, native speakers agree on which interpretations it can and cannot have. GPT-4 does not have the same capacity, as far as I've been able to determine. This one is an expectation, not even a factual statement. A factual statement would be "95% of English native speakers with a college degree" or so. Among less educated, the numbers could be depressingly low, even for much more straightforward tasks. Then, the question is how a given ML model fares against real data, not against some platonic ideal.

foobarqux 2y ago

First, Chomsky (and I) were talking about language acquisition in children where there aren't billions of examples, so it's completely irrelevant if some other system can do something, the question is how do humans do it. Second, there isn't any evidence that LLMs have captured grammar rules in any meaningful sense, just as they can't do addition or any other recursive computation.

numpad0 2y ago

Don't waste your time arguing Chomsky supporters, it's a cult. They keep wringing non-falsifiable theories out of lengthy hallucinations, but it all reveals itself to be just Trek physics later on. He's not just problematic in politics, he is in linguistics too, just the BS is harder to notice. I suppose his compiler theories were legit, and his pioneering spirit that lead to establishment of multiple fields of researches might be too, but leading theories he'd created are just as dubious as the first man made shed built on a discovered island would be. And the problem is not just that early speculations in a novel field are often wrong, it's that his supporters don't care. They'd regurgitate those scientific theories(tm) ad infinitum and waste resources for the whole humanity. So don't bother trying to fix them and making them motivated.