You could do this with logprobs. The language model itself has basically no real insight into its confidence but there’s more that you can get out of the model besides just the text.
The problem is that those probabilities are really “how confident are you that this text should come next in this conversation” not “how confident are you that this text is true/accurate.” It’s a fundamental limitation at the moment I think.
I feel like this isn’t quite true and is something I hear a lot of people say about ai. That it’s good at following requirements and confirming and being a mechanical and logical robot because that’s what computers are like and that’s how it is in sci fi.
In reality, it seems like that’s what they’re worst at. They’re great at seeing patterns and creating ideas but terrible at following instructions or staying on task. As soon as something is a bit bigger than they can track context for, they’ll get “creative” and if they see a pattern that they can complete, they will, even if it’s not correct. I’ve had copilot start writing poetry in my code because there was a string it could complete.
Get it to make a pretty looking static web page with fancy css where it gets to make all the decisions? It does it fast.
Give it an actual, specific programming task in a full sized application with multiple interconnected pieces and strict requirements? It confidently breaks most of the requirements, and spits out garbage. If it can’t hold the entire thing in its context, or if there’s a lot of strict rules to follow, it’ll struggle and forget what it’s doing or why. Like a particularly bad human programmer would.
This is why AI is automating art and music and writing and not more mundane/logical/engineering tasks. Great at being creative and balls at following instructions for more than a few steps.
Also “Thou mayest blame” and “Canst thou say”
Hurts my brain a little.