7 ms·

> In other words, we must get, from a multitude of possible interpretations of the above question, the one and only one meaning that, according to our commonsen

> In other words, we must get, from a multitude of possible interpretations of the above question, the one and only one meaning that, according to our commonsense knowledge of the world, is the one thought behind the question some speaker intended to ask. But, can humans do this? I think not; I still disagree with the author about what "Do we have a retired BBC reporter that was based in an East European country during the Cold War?", translated into code, means. They write "Doing the correct quantifier scoping: we are looking not for ‘a’ (single) reporter who worked in ‘some’ East European country, but to any reporter that worked in any East European country" My interpretation of this requirement is that they want a list of all the reporters who meet the criteria. However, I would probably write this query to return a boolean, not a list of reporters. And even if my interpretation is wrong... well, my point is still correct, because I failed to extract the "the one and only one meaning" that the author intended from that sentence. Even humans are only probably approximately correct.

bopbeepboop 5y ago
I’m confused, but think I concur with you. The two phrasings read as identical to me: - do we have a reporter who worked in some East European country? - is there any reporter (among ours) who worked in any East European country? - exists? (reporter in our-reporters) where (reporter.base in east-european-countries) I’m confused where the difference is supposed to be, between them. For every case, naming a person for whom that is true is a witness “yes” is correct; and being unable to is “no” being correct.
burnte 5y ago
> Even humans are only probably approximately correct. This is very true, more true than we realize. Notice how much more "could you repeat that?" we have with masks on. It's not JUST the mild muffling of the speaker's voice, it's not seeing their lips move. We're all lip readers to a small degree, and it helps inform our decoding to see the lips. Fff and th sound similar but look very different. Even without that, think if your life, ever said "Waht was that? Oh, right..." then reply. At first you missed part of what they said (for various reasons), but you were able to "interpolate" the missing part from the context, and most of the time you get it right. Our communication modes are lossy, and our brains make up for that to a large degree. That's the hole in natural language decoding, figuring out the hinting needed for an engine, because we're not totally aware of how we do it ourselves.
- emodendroket 5y ago
  That's a different problem, isn't it? That's more about transcription -- getting the speech into words -- than about what he's talking about, making sense of the words once you have them.
  - drdeca 5y ago
    The two tasks are interconnected. The reasoning flows both ways.
    - h0l0cube 5y ago
      Noise and lack of specificity are completely different problems, and the article concerns itself with the latter
      - jacobr1 5y ago
        A noisy word might easily be guessed in context. But likewise a semantically ambiguous word might also be guessed due to other factors like the tone of the speaker, facial expressions or more. I suspect the parent's point is that the disambiguation in both cases might be addressed with information encoded in the other. One can pattern match based on the context. I think some of the work in multimodal transformer models demonstrates this.
        h0l0cube 5y ago
        I think what you're saying is true, but I don't think this kind of ambiguity was really a feature of the examples in the article
      - burnte 5y ago
        I was replying specifically to the line in the comment I quoted, not the article as a whole.
    - emodendroket 5y ago
emodendroket 5y ago
Well, that's true, but I don't think we can pretend that computers are anywhere near as good at humans at extracting the intended meaning from human utterances.
amirkdv 5y ago
> But, can humans do this? I think not; > Even humans are only probably approximately correct. Fair point. But how about this: It's true that what John would do with the sentence is technically an "approximation" of what Alice would do because they have slightly different understandings of correct behavior. However, for humans to do what they do, they still do build an absolutely correct model of meaning in their mind wrt their (subjective) notion of correctness. This may sound like an obtuse play with words but the point is that to even attempt to do the right kind of reasoning in NLU, you need a different framework than PAC. You can't for example approximate whether "during the Cold War" qualifies "was based in" or qualifies "an Eastern European country". You just need to decide. And once you decide, you have an absolute correct interpretation, not an approximate one. EDIT: wording.
- nonameiguess 5y ago
  This seems to be misunderstanding what PAC learnable means. It's saying that for parameters epsilon and delta, you can get an algorithm for learning a hypothesis that, with probability 1 - delta, has an average error rate less than epsilon, given some number of training samples polynomial in 1/delta and 1/epsilon. This has nothing to do with the mental model. The language model doesn't have to spit out probabilities. It can use a simple thresholding function and spit out certainties. They just might be wrong, but a human's "certain" mental models might be wrong, too.
  - qayxc 5y ago
    There's a very important difference, though: humans can and often do qualify their interpretation. One major flaw of current PAC-based models is the inability to express certainty and being able to present the rationale behind their (lack of) confidence. One crucial aspect of human language processing is the ability to question or interrogate oneself to adjust one's interpretation. During a dialog this can be done by actively asking for more context or confirmation. So unlike current ML models, humans know that their interpretation/models might be wrong and can actively seek confirmation or correction. A trivial example would be asking a digital assistant "What's the capital of Georgia?" There are two possible answers and there's no way of knowing what the "correct" one is without further context. So a human would likely either ask whether you mean the country of Georgia or the US state, or qualify their answer ("the capital of the country/the state capital is Tbilisi/Atlanta"). This is what GPT-J-6B returns: Q: What's the capital of Georgia? A: Atlanta When given just the prompt "What's the capital of Georgia", GPT-J-6B also returns Atlanta and generates further text about the US state and its capital. And it's not as if the model doesn't "know": Q: What is Tbilisi? A: Tbilisi is the capital of Georgia. It's even more interesting when logic is involved: Q: If Albert is a pig and all pigs are blue, what colour is Albert? A: Albert is a blue pig. I only asked for the colour, but close enough. Can GPT deal with ambiguity? Q: If Albert is a pig and all pigs are blue, is Albert happy? A: No. Awesome! How about some common sense then: Q: If Alice is a dog and Bob just finished high school, can they have kids together? A: Yes, but they will have to get married first. So yeah, GPT obviously has no actual language understanding. Bob having finished high school implies that he's a young human man while Alice is a dog. Humans and dogs can't have kids together. But if the model doesn't understand language, why did it do so well on the logic test? In order to find out, I just asked :) Q: If Albert is a pig and all pigs are blue, why is Albert not happy? A: Because he is a pig. Hm. So are all pigs unhappy then? Let's take them out of the equation and ask again: Q: If Albert is blue, is Albert unhappy? A: No, Albert is not unhappy. Bingo! So the model only appeared to know the subtlety of "blue" having multiple meanings. It's as dumb as a rock and simply tried to match the tokens "is blue" against "is unhappy". Replacing "blue" with "pig" confirms this: Q: If Albert is a pig, is Albert unhappy? A: No, Albert is not unhappy. Albert is a pig. Dad humour. Well, either that or just a dumb model capable of fooling us humans because we tend to interpret more intelligence into its replies than is actually there... EDIT: in case you want to test this yourself (you might get different results, it's probabilistic after all): https://6b.eleuther.ai https://6b.eleuther.ai When using the Q/A-format, make sure not add a space after the "A:" prompt.
2muchcoffeeman 5y ago
Maybe your computer science brain kicked in took a CA interpretation of the question once they mentioned a query. Eg: Q: Are you a man or a woman? A: Yes Despite your interpretations of the article’s question. I bet you know which one is the more likely answer.
nonameiguess 5y ago
I don't think you interpreted the sentence in question differently, just the nature of how it is being translated into a query. They're talking specifically of how to translate into a query over a quantified relational algebra. Relational algebra always operates on and return sets. I'm pretty sure the author here is still looking for a boolean, but you can't ask a relational query engine does a result exist? You can only get back sets. The further question of whether or not the set is nonempty is an extra feature probably built into most real-world implementations of query engines, but it's not part of relational algebra. The actual problem they're identifying here is that of deciding how or whether to turn 'a' into a quantifier. Do we have 'any' would be less ambiguous, definitely an existential quantifier. Do we have 'a' depends on context, as it could mean one specific person and not be a quantifier at all, but I actually still disagree with the author here. I think they're right that English-speaking humans will unambiguously understand 'a' in this context to be a quantifier, but given that is possible at all, I don't see why a large language model can't. If it was actually being used to specify one person, the additional context would be in the sentence itself, as in 'do we have a reporter named X' or something like that. Given there is no additional qualification in the sentence, I'm pretty sure existing large language models can easily figure out what it means, and the only case in which they'd be wrong is if there were additional context outside of the sentence, but a human would be wrong too if all they heard was the sentence.

bopbeepboop 5y ago

I’m confused, but think I concur with you. The two phrasings read as identical to me: - do we have a reporter who worked in some East European country? - is there any reporter (among ours) who worked in any East European country? - exists? (reporter in our-reporters) where (reporter.base in east-european-countries) I’m confused where the difference is supposed to be, between them. For every case, naming a person for whom that is true is a witness “yes” is correct; and being unable to is “no” being correct.

burnte 5y ago

> Even humans are only probably approximately correct. This is very true, more true than we realize. Notice how much more "could you repeat that?" we have with masks on. It's not JUST the mild muffling of the speaker's voice, it's not seeing their lips move. We're all lip readers to a small degree, and it helps inform our decoding to see the lips. Fff and th sound similar but look very different. Even without that, think if your life, ever said "Waht was that? Oh, right..." then reply. At first you missed part of what they said (for various reasons), but you were able to "interpolate" the missing part from the context, and most of the time you get it right. Our communication modes are lossy, and our brains make up for that to a large degree. That's the hole in natural language decoding, figuring out the hinting needed for an engine, because we're not totally aware of how we do it ourselves.

emodendroket 5y ago
That's a different problem, isn't it? That's more about transcription -- getting the speech into words -- than about what he's talking about, making sense of the words once you have them.
- drdeca 5y ago
  The two tasks are interconnected. The reasoning flows both ways.
  - h0l0cube 5y ago
    Noise and lack of specificity are completely different problems, and the article concerns itself with the latter
    - jacobr1 5y ago
      A noisy word might easily be guessed in context. But likewise a semantically ambiguous word might also be guessed due to other factors like the tone of the speaker, facial expressions or more. I suspect the parent's point is that the disambiguation in both cases might be addressed with information encoded in the other. One can pattern match based on the context. I think some of the work in multimodal transformer models demonstrates this.
      - h0l0cube 5y ago
        I think what you're saying is true, but I don't think this kind of ambiguity was really a feature of the examples in the article
    - burnte 5y ago
      I was replying specifically to the line in the comment I quoted, not the article as a whole.
  - emodendroket 5y ago

emodendroket 5y ago

Well, that's true, but I don't think we can pretend that computers are anywhere near as good at humans at extracting the intended meaning from human utterances.

amirkdv 5y ago

> But, can humans do this? I think not; > Even humans are only probably approximately correct. Fair point. But how about this: It's true that what John would do with the sentence is technically an "approximation" of what Alice would do because they have slightly different understandings of correct behavior. However, for humans to do what they do, they still do build an absolutely correct model of meaning in their mind wrt their (subjective) notion of correctness. This may sound like an obtuse play with words but the point is that to even attempt to do the right kind of reasoning in NLU, you need a different framework than PAC. You can't for example approximate whether "during the Cold War" qualifies "was based in" or qualifies "an Eastern European country". You just need to decide. And once you decide, you have an absolute correct interpretation, not an approximate one. EDIT: wording.

nonameiguess 5y ago
This seems to be misunderstanding what PAC learnable means. It's saying that for parameters epsilon and delta, you can get an algorithm for learning a hypothesis that, with probability 1 - delta, has an average error rate less than epsilon, given some number of training samples polynomial in 1/delta and 1/epsilon. This has nothing to do with the mental model. The language model doesn't have to spit out probabilities. It can use a simple thresholding function and spit out certainties. They just might be wrong, but a human's "certain" mental models might be wrong, too.
- qayxc 5y ago
  There's a very important difference, though: humans can and often do qualify their interpretation. One major flaw of current PAC-based models is the inability to express certainty and being able to present the rationale behind their (lack of) confidence. One crucial aspect of human language processing is the ability to question or interrogate oneself to adjust one's interpretation. During a dialog this can be done by actively asking for more context or confirmation. So unlike current ML models, humans know that their interpretation/models might be wrong and can actively seek confirmation or correction. A trivial example would be asking a digital assistant "What's the capital of Georgia?" There are two possible answers and there's no way of knowing what the "correct" one is without further context. So a human would likely either ask whether you mean the country of Georgia or the US state, or qualify their answer ("the capital of the country/the state capital is Tbilisi/Atlanta"). This is what GPT-J-6B returns: Q: What's the capital of Georgia? A: Atlanta When given just the prompt "What's the capital of Georgia", GPT-J-6B also returns Atlanta and generates further text about the US state and its capital. And it's not as if the model doesn't "know": Q: What is Tbilisi? A: Tbilisi is the capital of Georgia. It's even more interesting when logic is involved: Q: If Albert is a pig and all pigs are blue, what colour is Albert? A: Albert is a blue pig. I only asked for the colour, but close enough. Can GPT deal with ambiguity? Q: If Albert is a pig and all pigs are blue, is Albert happy? A: No. Awesome! How about some common sense then: Q: If Alice is a dog and Bob just finished high school, can they have kids together? A: Yes, but they will have to get married first. So yeah, GPT obviously has no actual language understanding. Bob having finished high school implies that he's a young human man while Alice is a dog. Humans and dogs can't have kids together. But if the model doesn't understand language, why did it do so well on the logic test? In order to find out, I just asked :) Q: If Albert is a pig and all pigs are blue, why is Albert not happy? A: Because he is a pig. Hm. So are all pigs unhappy then? Let's take them out of the equation and ask again: Q: If Albert is blue, is Albert unhappy? A: No, Albert is not unhappy. Bingo! So the model only appeared to know the subtlety of "blue" having multiple meanings. It's as dumb as a rock and simply tried to match the tokens "is blue" against "is unhappy". Replacing "blue" with "pig" confirms this: Q: If Albert is a pig, is Albert unhappy? A: No, Albert is not unhappy. Albert is a pig. Dad humour. Well, either that or just a dumb model capable of fooling us humans because we tend to interpret more intelligence into its replies than is actually there... EDIT: in case you want to test this yourself (you might get different results, it's probabilistic after all): https://6b.eleuther.ai https://6b.eleuther.ai When using the Q/A-format, make sure not add a space after the "A:" prompt.

2muchcoffeeman 5y ago

Maybe your computer science brain kicked in took a CA interpretation of the question once they mentioned a query. Eg: Q: Are you a man or a woman? A: Yes Despite your interpretations of the article’s question. I bet you know which one is the more likely answer.

nonameiguess 5y ago

I don't think you interpreted the sentence in question differently, just the nature of how it is being translated into a query. They're talking specifically of how to translate into a query over a quantified relational algebra. Relational algebra always operates on and return sets. I'm pretty sure the author here is still looking for a boolean, but you can't ask a relational query engine does a result exist? You can only get back sets. The further question of whether or not the set is nonempty is an extra feature probably built into most real-world implementations of query engines, but it's not part of relational algebra. The actual problem they're identifying here is that of deciding how or whether to turn 'a' into a quantifier. Do we have 'any' would be less ambiguous, definitely an existential quantifier. Do we have 'a' depends on context, as it could mean one specific person and not be a quantifier at all, but I actually still disagree with the author here. I think they're right that English-speaking humans will unambiguously understand 'a' in this context to be a quantifier, but given that is possible at all, I don't see why a large language model can't. If it was actually being used to specify one person, the additional context would be in the sentence itself, as in 'do we have a reporter named X' or something like that. Given there is no additional qualification in the sentence, I'm pretty sure existing large language models can easily figure out what it means, and the only case in which they'd be wrong is if there were additional context outside of the sentence, but a human would be wrong too if all they heard was the sentence.