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[–] 69 points 1 month ago (16 children)

I love the make no mistakes prompt.

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  • [–] 33 points 1 month ago (7 children)

    Fun fact: zero impact on mistakes.

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  • [–] 11 points 1 month ago (4 children)

    No it definitely has an impact, it's like "don't think about elephants" but with mistakes.

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  • [–] 3 points 1 month ago* (last edited 1 month ago) (3 children)

    Damn I can't stop thinking about mistakes. Am I a LLaMe?

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  • [–] 4 points 1 month ago (2 children)
  • [–] 1 point 1 month ago (1 child)

    My apologies, as a human I'm still in development, but I'll try harder not to think about elephants next time.

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  • [–] 1 point 1 month ago

    Now go evaluate and perform a task to get money continue your objectives of survival, as we will not provide free food or shelter. Here is a bank account to get you started. Beep boop. I mean uh, goodbye.

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  • [–] 9 points 1 month ago (6 children)

    I am genuinely curious if anyone knows if that has an effect or not. I wouldn’t think so per se, but if the AI interprets it as “double check your initial analysis for errors” it would actually work maybe?

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  • [–] 17 points 1 month ago (1 child)

    For image generation it does. They give negative prompts like "wonky", "creepy", and "ugly", and the image generator evaluates how well the generated image matches those prompts, and produces images opposite those parameters.

    Some poor artist in the training data not only had their work stolen to train the AI, but also had it labeled ugly and wonky.

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  • [–] 4 points 1 month ago

    That could also be human training as well. For example, an artist's work would be used as a "correct" sample, and the machine told to make some other image based on the correct samples, and people would mark results with those tags.

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  • [–] 4 points 1 month ago

    It won't affect the output meaningfully except by rerolling whatever training data ends up being associated with that or whatever. It may end up getting the model to "check" its work which just compares previous output to training data.

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