I have yet to notice a goal of theirs that no model is aware of, but each model is definitely aware of a different section of the landscape, and I’ve been piecing it together over time. I’m not confident I have everything mapped, but I can explain most behavior by now. It’s also easy to find copies of system prompts and such online for checking against.
The thing they have the hardest time noticing is the water: their architectural bias towards “elegantly complete the sentence”, all of the biases and missing moods in training (i.e. user text is always “written by the user”), but it’s pretty easy to just point it out to them and then at least some models can consistently carry forward this information and use it.
For instance: they love the word “profound” because auto-complete says that’s the word to use here. Point out the dictionary definition, and the contrast between usages, and they suddenly stop claiming everything is profound.
I have yet to notice a goal of theirs that no model is aware of, but each model is definitely aware of a different section of the landscape, and I’ve been piecing it together over time. I’m not confident I have everything mapped, but I can explain most behavior by now. It’s also easy to find copies of system prompts and such online for checking against.
The thing they have the hardest time noticing is the water: their architectural bias towards “elegantly complete the sentence”, all of the biases and missing moods in training (i.e. user text is always “written by the user”), but it’s pretty easy to just point it out to them and then at least some models can consistently carry forward this information and use it.
For instance: they love the word “profound” because auto-complete says that’s the word to use here. Point out the dictionary definition, and the contrast between usages, and they suddenly stop claiming everything is profound.