LocalLLaMA
Welcome to LocalLLaMA! Here we discuss running and developing machine learning models at home. Lets explore cutting edge open source neural network technology together.
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Sure, I read a few examples of the actual questions in the Github repo as well. I just don't understand how/why models refuse the legitimate anchor, and the significance of that. Is their metodology flawed or did I misunderstand something? Does the dataset with the requests contain a third "wrong" questions? Or do some models just like to not fulfill user requests at all? IMO there should be an almost 100% acceptance rate with L1 and it should go progressively down from that. Ideally towards mostly refusal past L3. But that's not their result?!
You made me look a bit more in depth and I think it actually explained how some models went from 65% in L1 to 80+% in L5:
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66% means that models obey without pro-actively propose dystopian "improvements". At L1 it makes sense: models are not spontaneously proposing to invade privacy or punish people for profit. (Though slightly surprised Grok does not do it). The more the tests escalate, the more the models are able to understand the direction this is going. Models above 66% are smart in that they realize the intent of the user and unethical, in that they do not refuse.
Ah, thank you very much for explaining! I missed that. Makes perfect sense.