• @froztbyte
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    396 months ago

    it’s almost like this thing has no internal conceptual representation! I know this can’t possibly be, millions of promptfans and prompfondlers have told me it can’t be so, but it sure does look that way! wild!

    • Kogasa
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      -36 months ago

      It must have some internal models of some things, or else it wouldn’t be possible to consistently make coherent and mostly reasonable statements. But the fact that it has a reasonable model of things like grammar and conversation doesn’t imply that it has a good model of literally anything else, which is unlike a human for whom a basic set of cognitive skills is presumably transferable. Still, the success of LLMs in their actual language-modeling objective is a promising indication that it’s feasible for a ML model to learn complex abstractions.

      • @sc_griffith
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        266 months ago

        if I copy a coherent sentence into my clipboard, my clipboard becomes capable of consistently making coherent statements

        • Kogasa
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          6 months ago

          Yes, but that’s not how LLMs work. My statement depends heavily on the fact that a LLM like GPT is coaxed into coherence by unsupervised or semi-supervised training. That the training process works is the evidence of an internal model (of language/related concepts), not just the fact that something outputs coherent statements.

          • @selfA
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            146 months ago

            let me free up some of your time so you can go figure out how LLMs actually work

          • @sc_griffith
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            6 months ago

            if I have a bot pick a random book and copy the first sentence into my clipboard, my clipboard becomes capable of consistently making coherent statements. unsupervised training 👍

          • adderaline
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            126 months ago

            this isn’t necessarily true. patterns in data aren’t by nature proof of an underlying system of logic. if you run the line-fitting machine on any kind of data, its going to output a line. considering just how much data is encoded into these transformers, i don’t think we can conclusively say that it has a underlying conception of how language works, much less an understanding of the concepts that language represents. it could really just be using the vast quantities of data it has to output approximately correct statements. there’s absolutely structure there, but it doesn’t have to have the kind of structured understanding humans have about language to produce language, in the same way a less sophisticated machine learning model doesn’t have to know what kind of data its fitting a line to to make a line.

      • flere-imsaho
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        166 months ago

        it doesn’t. that’s why we’re calling it “spicy autocompletion” .

        • Kogasa
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          -76 months ago

          It does, which is why it’s autocompletion and not auto-gibberish.

      • @slopjockey
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        156 months ago

        It must have some internal models of some things, or else it wouldn’t be possible to consistently make coherent and mostly reasonable statements.

        Talk about begging the question