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.
I never said it can’t understand it. I am agreeing with the notion that it has a bias against using it.
You said it’s rarely used online, which just isn’t true.
But like even this:
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.
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.
BUT IM DETERMINED TO BE OFFENDED ON SOMEONE ELSE’S BEHALF