They do know what they don't know. There's a probability distribution for outputs that they are sampling from. That just isn't being used for that purpose.
To get calibrated probabilities, you actually need to use calibration techniques, and it is extremely unclear if any frontier models are doing this (or even how calibration can be done effectively in fancy chain-of-thought + MoE models, and/or how to do this in RLVR and RLHF based training regimes). I suppose if you get into things like conformal prediction, you could ensure some calibration, but this is likely too computationally expensive and/or has other undesirable side-effects.
EDIT: Oh and also there are anomaly detection approaches, which attempt to identify when we are in outlier space based on various (e.g. distance) metrics based on the embeddings, but even getting actual probabilities here is tricky. This is why it is so hard to get models to say they "don't know" with any kind of statistical certainty, because that information isn't generally actually "there" in the model, in any clean sense.
It is too computationally expensive, which is why nobody does this for production inference. But there are alignment tools to extract out these latent-space probabilities for researchers in the frontier labs.
They really aren't, especially if you consider the chain of thought / recursive application case, and also that you can't even assume e.g. a difference of 0.1 in softmax values means the same relative difference from input to input, or that e.g. an 0.9 is always "extremely confident", and etc. You really have no idea unless you are testing the calibration explicitly on calibration data.
> But there are alignment tools to extract out these latent-space probabilities for researchers in the frontier labs
You can get embeddings: if you can get calibrated probabilities, you'll need to provide a citation, because this would be a huge deal for all sorts of applications.
And now I'm certain we're taking past each other. I'm not talking about calibrated probabilities at all. Just the notion of "how confident do I feel about this?" which is what I interpreted the question above to be about. You can get that out of an LLM, with some work.
There is nothing straightforward about this, and no, there is no such formula.
> I'm not talking about calibrated probabilities at all. Just the notion of "how confident do I feel about this?"
If all you care about is vibes / feels, sure. If you actually need numerical guarantees and quantitative estimates to make your "feelings" about confidence mean something to rigorously justify decisions, you need calibration. If you aren't talking about calibration in these discussions, you are missing probably the most core technical concept that addresses these issues seriously.
Finding a way to objectively calibrate a sense of "how confident do I feel about this?" would be fantastic. But let's not move goal posts. It would still be incredibly useful to have a machine that can merely matches the equivalent statement of confidence or uncertainty that a human would assign to their mental model, even if badly calibrated.
> It would still be incredibly useful to have a machine that can merely matches the equivalent statement of confidence or uncertainty that a human would assign to their mental model, even if badly calibrated.
If human feelings are badly calibrated, they are useless here too, so no, I don't agree. Things like "confidence" only matter if they are actually tied to real outcomes in a consistent way, and that means calibration.
I'm pretty sure they are actively trained to avoid it.
Besides, like, what would you do if you asked your $200/mo AI something and it blanked on you?
I'm not sure who is doing what training exactly, but I can say that (inconsistently!) some of my attempts to get it to solve problems that have not yet actually been solved, e.g. the Collatz conjecture, have it saying it doesn't know how to solve the problem.
Other times it absolutely makes stuff up; fortunately for me, my personality includes actually testing what it says, so I didn't fall into the sycophantic honey trap and take it seriously when it agreed with my shower thoughts, and definitely didn't listen when it identified a close-up photo of some solanum nigrum growing next to my tomatoes as being also tomatoes.
> Besides, like, what would you do if you asked your $200/mo AI something and it blanked on you?
I'd rather it said "IDK" than made some stuff up. Them making stuff up is, as we have seen from various news stories about AI, dangerous.
> In this experiment, however, the model recognizes the injection before even mentioning the concept, indicating that its recognition took place internally.
If you ask me “how certain are you that the standard model of particle physics is true?” I’ll answer “I don’t know” because I don’t have any subject matter expertise, and philosophically I tend to hedge on questions like this anyway (“all models are wrong, some are useful”).
However, if you ask me “how certain are you that food is bland with no salt added, tastes better with some salt, but tastes bad with too much salt?” I would answer “very certain” because I have loads of direct experiments on this question in the kitchen. Furthermore, between these two extremes
To an LLM these are identical kinds of questions. All evidence has the same provenance: the training set. As of yet, we don’t have embodied AIs (robots) with multi-modal sensory inputs and online training. Until then, what we have remains a “brain in a vat fed on tokens” which, to me, is extremely weak from an epistemic perspective.
Is it the token (or set of tokens) that are strictly > 50% probable or is it just the highest probability in a set of probabilities?
While generating bullshit is not ideal for a lot of use cases you don't want your premier chat bot to say "I don't know" to the general public half the time. The investment in these things requires wide adoption so they are always going to favour the "guesses".
But it then throws that distribution away / consumes it in the next token calculation. So it's not really tracking it per se.