Okay, makes more sense now, now my understanding is that for question X, answer from ML system Y, amplification system A, verification in your quote is asking the A to answer “Would A(Z) output answer Y to question X?”, as opposed to asking A to answer “X”, and then checking if it equals “Y”. This can at most be as hard as running the original system, and maybe could be much more efficient.
Yep; that’s what I was imagining. It is also worth noting that it can be less safe to do that, though, since you’re letting A(Z) see Y, which could bias it in some way that you don’t want—I talk about that danger a bit in the context of approval-based amplification here and here.
Okay, makes more sense now, now my understanding is that for question X, answer from ML system Y, amplification system A, verification in your quote is asking the A to answer “Would A(Z) output answer Y to question X?”, as opposed to asking A to answer “X”, and then checking if it equals “Y”. This can at most be as hard as running the original system, and maybe could be much more efficient.
Yep; that’s what I was imagining. It is also worth noting that it can be less safe to do that, though, since you’re letting A(Z) see Y, which could bias it in some way that you don’t want—I talk about that danger a bit in the context of approval-based amplification here and here.