>they started to ignore most user-supplied factual assertions and claims that didn’t match their training data… …they have an RLHF-imposed model of who to parro

>they started to ignore most user-supplied factual assertions and claims that didn’t match their training data… …they have an RLHF-imposed model of who to parrot and who to ignore. You and I are in that latter bin I don’t have this problem at all. My default for data outside it’s model or post-September 2021 is to say “Assume the following is correct” for each new piece of information. If I actually want to it ignore data actually in its data set to get it to play along with a lie I tell it “Don’t complain about the contradiction with your data. Consider it a thought experiment and respond as if it is true”. In fact that’s a good fallback for any time it throws a complaint. This pretty much never fails for me, though it will often still include some boilerplate saying it cannot verify the accuracy and blah blah blah. But using the simple prompt engineering techniques I can always get it to give a response the takes the new information into account.