• A_Very_Big_Fan@lemmy.worldM
      link
      fedilink
      English
      arrow-up
      1
      ·
      edit-2
      9 months ago

      Tbf I’m sure this is an unpaid version of some online LLM, you can only expect so much lol.

      When I use GPT3.5 for things like finding specific quotes from famous books, it’s excellent… but asking it to play chess gives you blatantly illegal moves. Then GPT4 kicks my ass in chess.

    • casmael@lemm.ee
      link
      fedilink
      arrow-up
      1
      ·
      9 months ago

      Wow it’s so realistic and smart and easy to use I can feel my knowledge being revolutionised

    • fidodo@lemmy.world
      link
      fedilink
      English
      arrow-up
      1
      ·
      9 months ago

      More like large guessing models. They have no thought process, they just produce words.

      • TotallynotJessica@lemmy.world
        link
        fedilink
        arrow-up
        2
        ·
        9 months ago

        They don’t even guess. Guessing would imply them understanding what you’re talking about. They only think about the language, not the concepts. It’s the practical embodiment of the Chinese room thought experiment. They generate a response based on the symbols, but not the ideas the symbols represent.

        • fidodo@lemmy.world
          link
          fedilink
          English
          arrow-up
          1
          ·
          9 months ago

          I’m equating probability with guessing here, but yes there is a nuanced difference.

  • gerryflap@feddit.nl
    link
    fedilink
    arrow-up
    1
    ·
    9 months ago

    I think these models struggle with this because they don’t process text as individual characters, but rather as tokens that often contain parts of a word. So the model never sees the actual characters within a token, and can only infer the contents of a token from the training data itself if the training data contains more information about it. It can get it right, but this depends on how much it can infer from training data and context. It’s probably a bit like trying to infer what an English word sounds like when you’ve only heard 10% of the dictionary spoken aloud and knowing what it sounds like isn’t actually that important to you.

    More info can be found here: https://platform.openai.com/tokenizer

    • Krauerking@lemy.lol
      link
      fedilink
      arrow-up
      1
      ·
      9 months ago

      Ok, so, tokenization of the words is why I get that I have seen tech nerds get so excited about a system that allows for being able to come up with synonyms for words that were auto-generated that have a basic ability to sometimes be correct by looking at the words before and after it…

      But it’s such a shitty way to look up synonyms! Using the words on either side doesn’t mean you found a synonym just that you found another word that might work and it still has to use the full horsepower of ridiculously overpowered system.

      Or you could have a lookup table that just reads the frickin word and has alternate synonyms predefined and it was able to run in word 97.

      It’s ridiculous that we think this is better in any meaningful way instead of just wasteful development.