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The point isn't that it can translate between languages. It's not translating the instructions, at least not explicitly. Here's what they did: - They found a t

by _dain_ 4y ago

The point isn't that it can translate between languages. It's not translating the instructions, at least not explicitly. Here's what they did: - They found a task that GPT wasn't very good at, because examples of that task weren't in the training set (in any language). - They trained a fine-tuned variant of GPT where examples of the task were in an appended training set, but only in English. - They told the variant to do that task again, in other languages. - Its performance improved on all of them, not just English If the training set has no Mandarin examples of the task, how did it get better when you ask it in Mandarin? Sure, you could fake this by having an API call to Google Translate where it takes the Mandarin request, translates to English, solves the task in English, then Google Translates back to Mandarin. But it's not doing that, it's just doing the same "predict the next token" operation on the Mandarin prompt. I don't see how it can do that unless it really has some kind of understanding.

ivanbakel 4y ago
>I don't see how it can do that unless it really has some kind of understanding. One possibility is that the model itself has learned that tokens are related across languages based on translation examples. If the appended training changes the model's treatment of tokens in one language, that could have a statistical knock-on effect on the weights between similar tokens in different languages. Similarly, if you train the model that "blue" is a "colour", you'd expect it to pick up that "navy" is a "shade".
- _dain_ 4y ago
  At the scale/complexity GPT operates at, how is that different from "understanding"? It seems like you've just rephrased it but with more words.
  - majewsky 4y ago
    Yeah, but then it goes back to GP's original argument. If relations between translated tokens are classified as "understanding", that would mean that translation AIs are already capable of understanding: > in which case you could just use machine translation as your example to show that some computational model is capable of understanding, and leave ChatGPT out of it.
    - _dain_ 4y ago
      Show me a pre-GPT machine translation model that can do what I've described.
      - deleted 4y ago
        [deleted]
  - ivanbakel 4y ago
    Because it's a purely statistical explanation that doesn't require understanding. Put differently, it's possible that GPT doesn't "understand" language itself as a concept, and instead tokens in the same language are just highly-correlated when it comes to prediction. When affecting weights between tokens, it wouldn't be surprising that those weights have effects across languages, much in the same way they work within languages - after all, it's all just probabilities to GPT. Google Translate works in a comparable way, and nobody suggests that it is sentient. Frankly, the argument that "GPT operates at a scale that means it must be sentient" is begging the question.
    - _dain_ 4y ago
      No, you're the one who's begging the question. Why can't a "purely statistical" process have an understanding? If you a priori assume it can't, then nothing could ever persuade you GPT understood anything, no matter how it performed. And again, this magical word "just". "Just highly correlated", "just probabilities". Putting the word "just" in front of something doesn't mean you've explained it.
      - ivanbakel 4y ago
        >No, you're the one who's begging the question. Why can't a "purely statistical" process have an understanding? This is a related, but fundamentally different thing to the point I replied to in your original comment. You asked: > I don't see how it can [apply training across languages] unless it really has some kind of understanding. I provided a potential explanation that is in line with how we think GPT works, and which doesn't require it to have understanding. You may feel that GPT is complex enough that this process itself models understanding - but I disagree, and I think that's begging the question because it falls back on a fact (GPT is highly complex) that is independent of the above problem (how GPT applied training across languages.) I am not compelled by the translation example to believe beyond doubt that GPT actually models and understands abstract concepts. I don't think the fact that its training works across languages is any proof that it parses that training in an abstract way, or that it forms abstract links between the same ideas in different languages, or indeed that it has any notions of language at all.
  - sdifnhiono 4y ago
    The fact that something is hard to define does not mean it isn't meaningful. I cannot tell you what consciousness is, but I know that I have it and my sock does not. It is trivial to tie these machines up in knots, blatantly contradicting themselves from one sentence to the next. If that is understanding then it is an understanding utterly foreign to any human, and a form of understanding that looks a whole lot like bullshit.

by _dain_ 4y ago

ivanbakel 4y ago

>I don't see how it can do that unless it really has some kind of understanding. One possibility is that the model itself has learned that tokens are related across languages based on translation examples. If the appended training changes the model's treatment of tokens in one language, that could have a statistical knock-on effect on the weights between similar tokens in different languages. Similarly, if you train the model that "blue" is a "colour", you'd expect it to pick up that "navy" is a "shade".

_dain_ 4y ago
At the scale/complexity GPT operates at, how is that different from "understanding"? It seems like you've just rephrased it but with more words.
- majewsky 4y ago
  Yeah, but then it goes back to GP's original argument. If relations between translated tokens are classified as "understanding", that would mean that translation AIs are already capable of understanding: > in which case you could just use machine translation as your example to show that some computational model is capable of understanding, and leave ChatGPT out of it.
  - _dain_ 4y ago
    Show me a pre-GPT machine translation model that can do what I've described.
    - deleted 4y ago
      [deleted]
- ivanbakel 4y ago
  Because it's a purely statistical explanation that doesn't require understanding. Put differently, it's possible that GPT doesn't "understand" language itself as a concept, and instead tokens in the same language are just highly-correlated when it comes to prediction. When affecting weights between tokens, it wouldn't be surprising that those weights have effects across languages, much in the same way they work within languages - after all, it's all just probabilities to GPT. Google Translate works in a comparable way, and nobody suggests that it is sentient. Frankly, the argument that "GPT operates at a scale that means it must be sentient" is begging the question.
  - _dain_ 4y ago
    No, you're the one who's begging the question. Why can't a "purely statistical" process have an understanding? If you a priori assume it can't, then nothing could ever persuade you GPT understood anything, no matter how it performed. And again, this magical word "just". "Just highly correlated", "just probabilities". Putting the word "just" in front of something doesn't mean you've explained it.
    - ivanbakel 4y ago
      >No, you're the one who's begging the question. Why can't a "purely statistical" process have an understanding? This is a related, but fundamentally different thing to the point I replied to in your original comment. You asked: > I don't see how it can [apply training across languages] unless it really has some kind of understanding. I provided a potential explanation that is in line with how we think GPT works, and which doesn't require it to have understanding. You may feel that GPT is complex enough that this process itself models understanding - but I disagree, and I think that's begging the question because it falls back on a fact (GPT is highly complex) that is independent of the above problem (how GPT applied training across languages.) I am not compelled by the translation example to believe beyond doubt that GPT actually models and understands abstract concepts. I don't think the fact that its training works across languages is any proof that it parses that training in an abstract way, or that it forms abstract links between the same ideas in different languages, or indeed that it has any notions of language at all.
- sdifnhiono 4y ago
  The fact that something is hard to define does not mean it isn't meaningful. I cannot tell you what consciousness is, but I know that I have it and my sock does not. It is trivial to tie these machines up in knots, blatantly contradicting themselves from one sentence to the next. If that is understanding then it is an understanding utterly foreign to any human, and a form of understanding that looks a whole lot like bullshit.