2 ms·

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-f6bea82a2

by 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
    There is no example or standard that would satisfy you. Any failing example can be added to the training set in the next version and even if it couldn't it wouldn't matter because you could find a person somewhere that would also fail it.
  - foldr 2y ago
    We can see similar results with much simpler examples where the judgments are incontrovertible. Here's a classic pair from Chomsky: a. John expects to present himself. b. Who does John expect to present himself? In (a) 'himself' refers to 'John' whereas in (b) it refers to whichever person is the answer to the question. How does GPT-4o fare? > Who does "himself" refer to in the sentence "John expects to present himself"? > In the sentence "John expects to present himself," "himself" refers to John. It means that John is the one who expects to present himself. > Who does "himself" refer to in the sentence "Who does John expect to present himself?" > "Himself" refers to John. In this sentence, John is the one who is expected to present himself. The statistical model is confused by the superficial similarity between (a) and (b), just as Chomsky predicted decades ago.
    - stared 2y ago
      Well, again, I'm not sure what your prompts are, but here we go: https://chatgpt.com/c/7ead1985-83be-4dce-9fd7-29aafb248f01 https://chatgpt.com/c/7ead1985-83be-4dce-9fd7-29aafb248f01 > The statistical model is confused by the superficial similarity between (a) and (b), just as Chomsky predicted decades ago. Well, WE are statistical models as well. So, any too-broad claims on the inability to understand natural language by ANY statistical models are falsified the moment they are spoken (or written); unless you go into Penrose-style Neoplatonism. --- Vide your doubt if I find a (counter)convincing example. Sure, any benchmark of human vs. model performance is an empirical verification. And yes, some artificial models may struggle with some tasks. For example, for the Winograd schema, there is a leap between GPT3 and GPT4: https://d-kz.medium.com/evaluating-gpt-3-and-gpt-4-on-the-winograd-schema-challenge-reasoning-test-e4de030d190d https://d-kz.medium.com/evaluating-gpt-3-and-gpt-4-on-the-wi.... What it's (to me) is ex-cathedra defining what is the English language. The actual natural language people use is full of utterances that are not correct (yet are easily understandable) and "correct forms" that are rarely used and, if so, misunderstood by anyone save for those who put conscious effort (linguists, lawyers, etc). For any discussions, it is essential to know if we are working with a real (and highly probabilistic) natural language or what of its concrete models (i.e., an abstraction).

by stared 2y ago

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
  There is no example or standard that would satisfy you. Any failing example can be added to the training set in the next version and even if it couldn't it wouldn't matter because you could find a person somewhere that would also fail it.
- foldr 2y ago
  We can see similar results with much simpler examples where the judgments are incontrovertible. Here's a classic pair from Chomsky: a. John expects to present himself. b. Who does John expect to present himself? In (a) 'himself' refers to 'John' whereas in (b) it refers to whichever person is the answer to the question. How does GPT-4o fare? > Who does "himself" refer to in the sentence "John expects to present himself"? > In the sentence "John expects to present himself," "himself" refers to John. It means that John is the one who expects to present himself. > Who does "himself" refer to in the sentence "Who does John expect to present himself?" > "Himself" refers to John. In this sentence, John is the one who is expected to present himself. The statistical model is confused by the superficial similarity between (a) and (b), just as Chomsky predicted decades ago.
  - stared 2y ago
    Well, again, I'm not sure what your prompts are, but here we go: https://chatgpt.com/c/7ead1985-83be-4dce-9fd7-29aafb248f01 https://chatgpt.com/c/7ead1985-83be-4dce-9fd7-29aafb248f01 > The statistical model is confused by the superficial similarity between (a) and (b), just as Chomsky predicted decades ago. Well, WE are statistical models as well. So, any too-broad claims on the inability to understand natural language by ANY statistical models are falsified the moment they are spoken (or written); unless you go into Penrose-style Neoplatonism. --- Vide your doubt if I find a (counter)convincing example. Sure, any benchmark of human vs. model performance is an empirical verification. And yes, some artificial models may struggle with some tasks. For example, for the Winograd schema, there is a leap between GPT3 and GPT4: https://d-kz.medium.com/evaluating-gpt-3-and-gpt-4-on-the-winograd-schema-challenge-reasoning-test-e4de030d190d https://d-kz.medium.com/evaluating-gpt-3-and-gpt-4-on-the-wi.... What it's (to me) is ex-cathedra defining what is the English language. The actual natural language people use is full of utterances that are not correct (yet are easily understandable) and "correct forms" that are rarely used and, if so, misunderstood by anyone save for those who put conscious effort (linguists, lawyers, etc). For any discussions, it is essential to know if we are working with a real (and highly probabilistic) natural language or what of its concrete models (i.e., an abstraction).