We were reviewing reports of situations where the models failed to follow directions and there was a common thread of some where when the operator got the model to acknowledge the rule breach, it quoted back something that included swearing.
I don’t have the data to truely look into it, but I did give the instruction to my engineers to avoid it as a “might be a problem”.
But I avoid unnecessary emotion in my prompts because I don't want potentially distracting activations. Kind of like communicating with humans.
> impolite prompts consistently outperformed polite ones, with accuracy ranging from 80.8% for Very Polite prompts to 84.8% for Very Rude prompts.
Unless the mechanism is understood, my assumption is that this is a moving target.
https://www.anthropic.com/research/emotion-concepts-function
How so? Plenty of swearing in lots of training data, especially older code, e.g. in Linux.