[kgwgk]

It’s not “fairly trivial”.

The continuation of the phrase “George Washington was born” could be multiple things. You get a probability for the next token selected (for example “in”) and a probability for the token after that (for example “Virginia”) and you can multiply them to get the probability of the “in Virgina” response but what does it mean? Maybe the probability is low becase “on February …” is more likely.

If the first token was “in” you could end up with “in Virginia in 1732” or “in 1732 in Virginia” and both responses are in some sense the same but the probability of each one doesn’t take that into account. Et cetera.

[tempestn]

Yeah, I saw something similar in a reply to another comment. I don't think it would be quite as bad as that because it's not just completing the phrase in a vacuum though, but in the context of the prompt. So if the prompt was "where was GW born", then "in Virginia" would be much more likely than "in 1732". But I do understand that there would often be multiple ways to word the same thing, or multiple correct answers to the same prompt.

In the case of multiple wordings of the same thing, I wonder if there could be a way to determine closeness of responses, and consider them together when calculating confidence. As a simple example, if responses have the same rare words (like 1732) and differs only in the sentence order or the more common words ("in", etc.) used, those would be more similar than ones that used different rare words. So perhaps that could be accounted for.

As for multiple correct answers to the same prompt, I think that's fine. The confidence of a response might be low because it's one correct answer of many, or because the model has no idea and it's taking a wild-ass guess. But the user asking the question probably has an idea of whether what's being asked is very common knowledge or something obscure or controversial. At least much of the time. And even if the metric wasn't perfect, I still feel it could be useful.

Of course this is all the rambling of someone who doesn't really know anything about this stuff. You could just say I'm spitting out some likely tokens I guess; consider the confidence low.

[kgwgk]

You’re right, there are ways to tackle this problem but they may require some case-by-case effort to define what you are trying to find out and to incorporate information external to the model itself. Not fairly trivial :-)

[tempestn]

Ha, I mean it would be fairly trivial to output "a confidence factor of some sort". It just becomes less trivial when you try to actually make it useful!

[macrolime]

So you take the output, e.g. "George Washington was born in Virgina" and ask another prompt. Is the following true? Answer with a single word either true or false: "George Washington was born in Virgina". It will then output true/false with a probability, although for GPT-4 this is not available through the API.

[kgwgk]

Actually it's funny how you can ask the follow-up question "Are you sure?" and quite often GPT-4 will apologize and change a correct answer to give an incorrect one instead.