• givesomefucks@lemmy.world
    link
    fedilink
    English
    arrow-up
    9
    arrow-down
    30
    ·
    2 months ago

    Except it has no difficulty reading and understanding AAVE, because people use it online frequently…

    Like, the article makes that abundantly clear, but everyone commenting just read the headline and assumed what it meant was it couldn’t understand it…

    • bionicjoey@lemmy.ca
      link
      fedilink
      English
      arrow-up
      24
      arrow-down
      1
      ·
      2 months ago

      I never said it can’t understand it. I am agreeing with the notion that it has a bias against using it.

      • givesomefucks@lemmy.world
        link
        fedilink
        English
        arrow-up
        4
        arrow-down
        23
        ·
        2 months ago

        You said it’s rarely used online, which just isn’t true.

        But like even this:

        I am agreeing with the notion that it has a bias against using it

        I’m not sure if you understand the bias is against users who use AAVE, or if you’re saying a LLM doesn’t want to use AAVE.

        Maybe you did understand everything, and you’re just being vague.

        But almost everything you said could be interpreted multiple ways.

        • sugar_in_your_tea@sh.itjust.works
          link
          fedilink
          English
          arrow-up
          12
          arrow-down
          1
          ·
          2 months ago

          Well, if the training data is largely standard english, AAVE could look like less educated English, because it doesn’t follow the normal rules and conventions. And there’s probably a higher correlation between AAVE use and lower means and/or education because people from the black community who have higher means and/or education probably use standard English more often because that’s how they’re trained.

          So I don’t think this is evidence about the model being “racist” or anything of that nature, it’s just the model doing model things. If you type in AAVE, chances are higher that you fit the given demographic, because that’s likely what the training data shows.

          So, I guess don’t really see the issue here? This just sounds like people thinking the model does more than it does. The model merely matches input text to data in the model. That’s it. There’s no “understanding” here, it’s just matching inputs to outputs.

            • Mac@mander.xyz
              link
              fedilink
              English
              arrow-up
              1
              ·
              2 months ago

              There are times when it’s acceptable and even admirable to be offended on someone else’s behalf.
              I’m not sure this is one of those times.