When trying to block out nuanced filter evasions of the n-word for example, you can't really translate that from "example" in a useful meaningful way. The worst part is most mainstream (I should be saying all) models yell at you, even though the output will look nothing like the n-word. I figured an LLM would be a good way to get insanely nuanced about a regex.
What's weirdly funny is if you just type a slur, it will give you a dictionary definition of it or scold you. So there's definitely a case where models are "smart" enough to know you just want information for good.
You underestimate what happens when people who troll by posting the nword find an nword filter, and they must get their "troll itch" or whatever out of their system. They start evading your filters. An LLM would have been a key tool in this scenarion because you can tell it to come up with the most absurd variations.
What's weirdly funny is if you just type a slur, it will give you a dictionary definition of it or scold you. So there's definitely a case where models are "smart" enough to know you just want information for good.
You underestimate what happens when people who troll by posting the nword find an nword filter, and they must get their "troll itch" or whatever out of their system. They start evading your filters. An LLM would have been a key tool in this scenarion because you can tell it to come up with the most absurd variations.