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?
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.
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.
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.
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?
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.
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.
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.
Earlier LLMs it helped a bit.
Now a days the harnesses know to spawn ‘review’ agents which will catch some mistakes but not all.
Lol “know”. LLMs don’t know anything. It’s an advanced autocomplete.
That’s not fair, my auto complete knows to always replace duck with duck even when I try to fix it back to duck!