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No, training and inference are two separate processes. Training data is never redistributed, only obtained and analyzed. What matters is what data is put into c

No, training and inference are two separate processes. Training data is never redistributed, only obtained and analyzed. What matters is what data is put into context during inference. This is controlled by the user. AI/ML is complex, so as a simpler analogy: If I watch The Simpsons, and I create an amusing infographic of how often Homer says "D'oh!" over time, my infographic would be an original work. AI training follows the same principle.

jacquesm 7mo ago
> my infographic would be an original work. > AI training follows the same principle. If you really believe that then we can't have a meaningful conversation about this, that's not even ELIF territory, that's just disconnected. You should be asking questions, not telling people how it works.
- ndriscoll 7mo ago
  How exactly is it different? All the model itself is is a probability distribution for next token given input, fitted to a giant corpus. i.e. a description of statistical properties. On its own it doesn't even "do" anything, but even if you wrap that in a text generator and feed it literal gcc source code fragments as input context, it will quickly diverge. Because it's not a copy of gcc. It doesn't contain a copy of gcc. It's a description of what language is common in code in general. In fact we could make this concrete: use the model as the prediction stage in a compressor, and compress gcc with it. The residual is the extent to which it doesn't contain gcc.
  - jacquesm 7mo ago
    There already have been multiple documented cases of LLMs spitting out fairly large chunks of the input corpus. There have been some experiments to get it to replicate the entirety of 'Moby Dick' with some success for one model but less success with others most likely due to output filtering to prevent the generation of such texts, but that doesn't mean they're not in there in some form. And how could they not be, it is just a lossy compression mechanism, the degree of loss is not really all that relevant to the discussion.
    - ndriscoll 7mo ago
      Are you referring to this? https://osyuksel.github.io/blog/reconstructing-moby-dick-llm/ https://osyuksel.github.io/blog/reconstructing-moby-dick-llm... I see a test where one model managed to 85% reproduce a paragraph given 3 input paragraphs under 50% of the time. So it can't even produce 1 paragraph given 3 as input, and it can't even get close half the time. "Contains Moby Dick" would be something like you give it the first paragraph and it produces the rest of the book. What we have here instead is a statistical model that when given passages can do an okay job at predicting a sentence or two, but otherwise quickly diverges.
      - xyzzy_plugh 7mo ago
        I'm no longer certain what point you're trying to make. Getting close less than half the time given three paragraphs as input still sounds like red-handed copyright infringement to me. If I sample a copyrighted song in my new track, clip it, slow it down, and decimate the bit rate, a court would not let me off the hook. It doesn't matter how much context you push into these things. If I feed them 50% of Moby Dick and they produce the next word, and I can repeatedly do that to produce the entire book (I'm pretty sure the number of attempts is wholly irrelevant: we're impossibly far from monkeys on typewriters) then we can prove the statistical model encodes the book. The further we are from that (and the more we can generate with less) then the stronger the case is. It's a pretty strong case!
        ndriscoll 7mo ago
        That's... not how this works. > If I feed them 50% of Moby Dick and they produce the next word and I can repeatedly do that to produce the entire book... then we can prove the statistical model encodes the book. It can't because it doesn't. That's what it means to say it diverges. The "number of attempts" is you cheating. You're giving it the book when you let it try again word by word until it gets the correct answer, and then claiming it produced the book. That's exactly the residual that I said characterizes the extent to which it doesn't know the book. Trivially, no matter how bad the model is, if you give it the residual, it can losslessly compress anything at all. If you had a simple model that just predicts next word given current word (trained on word pair frequency across all English text, or even all text excluding Moby Dick), and then give it retries until it gets the current word right, it will also quickly produce the book. Because it was your retry policy that encoded the book, not the model. Without that policy, it will get it wrong within a few words, just like these models do.
  - gus_massa 7mo ago
    For an infographic, perhaps you claim claim fair use. I think it makes a lot of sense, but IANAL. For a fan fiction episode that is different from all official episodes, you may cross your fingers. For a remake of one of the episodes with a different camera angle and similar dialog, I expect that you will get in problems.
    - ndriscoll 7mo ago
      Is the claim that these models can 1 shot a Simpsons episode remake with different camera angle and similar dialog from a prompt like "produce Simpsons episode S01E04"? Or are we falling into the "the user doesn't notice that they told the model the answer, and the model in fact did not memorize the thing" trap?

jacquesm 7mo ago

> my infographic would be an original work. > AI training follows the same principle. If you really believe that then we can't have a meaningful conversation about this, that's not even ELIF territory, that's just disconnected. You should be asking questions, not telling people how it works.

ndriscoll 7mo ago
How exactly is it different? All the model itself is is a probability distribution for next token given input, fitted to a giant corpus. i.e. a description of statistical properties. On its own it doesn't even "do" anything, but even if you wrap that in a text generator and feed it literal gcc source code fragments as input context, it will quickly diverge. Because it's not a copy of gcc. It doesn't contain a copy of gcc. It's a description of what language is common in code in general. In fact we could make this concrete: use the model as the prediction stage in a compressor, and compress gcc with it. The residual is the extent to which it doesn't contain gcc.
- jacquesm 7mo ago
  There already have been multiple documented cases of LLMs spitting out fairly large chunks of the input corpus. There have been some experiments to get it to replicate the entirety of 'Moby Dick' with some success for one model but less success with others most likely due to output filtering to prevent the generation of such texts, but that doesn't mean they're not in there in some form. And how could they not be, it is just a lossy compression mechanism, the degree of loss is not really all that relevant to the discussion.
  - ndriscoll 7mo ago
    Are you referring to this? https://osyuksel.github.io/blog/reconstructing-moby-dick-llm/ https://osyuksel.github.io/blog/reconstructing-moby-dick-llm... I see a test where one model managed to 85% reproduce a paragraph given 3 input paragraphs under 50% of the time. So it can't even produce 1 paragraph given 3 as input, and it can't even get close half the time. "Contains Moby Dick" would be something like you give it the first paragraph and it produces the rest of the book. What we have here instead is a statistical model that when given passages can do an okay job at predicting a sentence or two, but otherwise quickly diverges.
    - xyzzy_plugh 7mo ago
      I'm no longer certain what point you're trying to make. Getting close less than half the time given three paragraphs as input still sounds like red-handed copyright infringement to me. If I sample a copyrighted song in my new track, clip it, slow it down, and decimate the bit rate, a court would not let me off the hook. It doesn't matter how much context you push into these things. If I feed them 50% of Moby Dick and they produce the next word, and I can repeatedly do that to produce the entire book (I'm pretty sure the number of attempts is wholly irrelevant: we're impossibly far from monkeys on typewriters) then we can prove the statistical model encodes the book. The further we are from that (and the more we can generate with less) then the stronger the case is. It's a pretty strong case!
      - ndriscoll 7mo ago
        That's... not how this works. > If I feed them 50% of Moby Dick and they produce the next word and I can repeatedly do that to produce the entire book... then we can prove the statistical model encodes the book. It can't because it doesn't. That's what it means to say it diverges. The "number of attempts" is you cheating. You're giving it the book when you let it try again word by word until it gets the correct answer, and then claiming it produced the book. That's exactly the residual that I said characterizes the extent to which it doesn't know the book. Trivially, no matter how bad the model is, if you give it the residual, it can losslessly compress anything at all. If you had a simple model that just predicts next word given current word (trained on word pair frequency across all English text, or even all text excluding Moby Dick), and then give it retries until it gets the current word right, it will also quickly produce the book. Because it was your retry policy that encoded the book, not the model. Without that policy, it will get it wrong within a few words, just like these models do.
- gus_massa 7mo ago
  For an infographic, perhaps you claim claim fair use. I think it makes a lot of sense, but IANAL. For a fan fiction episode that is different from all official episodes, you may cross your fingers. For a remake of one of the episodes with a different camera angle and similar dialog, I expect that you will get in problems.
  - ndriscoll 7mo ago
    Is the claim that these models can 1 shot a Simpsons episode remake with different camera angle and similar dialog from a prompt like "produce Simpsons episode S01E04"? Or are we falling into the "the user doesn't notice that they told the model the answer, and the model in fact did not memorize the thing" trap?