I bet it does have an impact on mistakes. I bet it tries harder to gaslight and hide them. Yeah, it doesn’t actually decrease the quantity of mistakes, but it does change how things are presented and makes it use words that convince you it’s actually considered things and won’t have made a mistake.
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 literally have Claude send every edit to another model to check and make sure it isn’t word barfing. Every file edit is a call to another model to make sure that edit doesn’t suck.
I love the make no mistakes prompt.
Yeah 'cause faulty children won’t make mistakes. 🤣
Fun fact: zero impact on mistakes.
I bet it does have an impact on mistakes. I bet it tries harder to gaslight and hide them. Yeah, it doesn’t actually decrease the quantity of mistakes, but it does change how things are presented and makes it use words that convince you it’s actually considered things and won’t have made a mistake.
No it definitely has an impact, it’s like “don’t think about elephants” but with mistakes.
Damn I can’t stop thinking about mistakes. Am I a LLaMe?
You are mistaken.
My apologies, as a human I’m still in development, but I’ll try harder not to think about elephants next time.
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.
You mean it will spawn agents to drive up the token costs and maybe fingers crossed catch some errors?
I’m on the 20 dollar a month z.ai plan, I’ve yet to hit the 5 hour limit. What token costs?
Claude I’d usually hit it in an hour at most lol
Correct
I literally have Claude send every edit to another model to check and make sure it isn’t word barfing. Every file edit is a call to another model to make sure that edit doesn’t suck.
Tokens++