Yeah good detail that they are instruct-finetuned too (say text-davinci-003) ... what I mean here is the two situations below in Figure 8 for the "Base model" a

Yeah good detail that they are instruct-finetuned too (say text-davinci-003) ... what I mean here is the two situations below in Figure 8 for the "Base model" and "RHLF model" (a difference I appreciate I did not highlight originally) as in Figure 8 of the paper. From reddit passing on a LessWrong comment: Paul Christiano, alignment researcher at ARC/ previously OpenAI was questioned by somebody: Perhaps I am misunderstanding Figure 8? I was assuming that they asked the model for the answer, then asked the model what probability it thinks that that answer is correct. Under this assumption, it looks like the pre-trained model outputs the correct probability, but the RLHF model gives exaggerated probabilities because it thinks that will trick you into giving it higher reward. And Paul replies: Yes, I think you are misunderstanding figure 8. I don't have inside information, but without explanation "calibration" would almost always mean reading it off from the logits. If you instead ask the model to express its uncertainty I think it will do a much worse job, and the RLHF model will probably perform similarly to the pre-trained model. (This depends on details of the human feedback, under a careful training regime it would probably get modestly better.)