Edit: There is also some other work that points out that chat models might not be calibrated at the token-level, but might be calibrated at the concept-level [2]. Which means that if you sample many answers, and group them by semantic similarity, that is also calibrated. The problem is that generating many answer and grouping them is more costly.
[1] https://arxiv.org/pdf/2303.08774 Figure 8
[2] https://arxiv.org/pdf/2511.04869 Figure 1.
You could color code the output token so you can see some abrupt changes
It seems kind of obvious, so I'm guessing people have tried this
Think of traditional statistics. Suppose I said "80% of those sampled preferred apples to oranges, and my 95% confidence interval is within +/- 2% of that" but then I didn't tell you anything about how I collected the sample. Maybe I was talking to people at an apple pie festival? Who knows! Without more information on the sampling method, it's hard to make any kind of useful claim about a population.
This is why I remain so pessimistic about LLMs as a source of knowledge. Imagine you had a person who was raised from birth in a completely isolated lab environment and taught only how to read books, including the dictionary. They would know how all the words in those books relate to each other but know nothing of how that relates to the world. They could read the line "the killer drew his gun and aimed it at the victim" but what would they really know of it if they'd never seen a gun?
I mean I sort of understand what you're trying to say but in fact a great deal of knowledge we get about the world we live in, we get second hand.
There are plenty of people who've never held a gun, or had a gun aimed at them, and.. granted, you could argue they probably wouldn't read that line the same way as people who have, but that doesn't mean that the average Joe who's never been around a gun can't enjoy media that features guns.
Same thing about lots of things. For instance it's not hard for me to think of animals I've never seen with my own eyes. A koala for instance. But I've seen pictures. I assume they exist. I can tell you something about their diet. Does that mean I'm no better than an LLM when it comes to koala knowledge? Probably!
Bringing pictures into the mix still doesn’t add anything, because the pictures aren’t any more connected to real world experiences. Flooding a bunch of images into the mind of someone who was blind from birth (even if you connect the images to words) isn’t going to make any sense to them, so we shouldn’t expect the LLM to do any better.
Think about the experience of a growing baby, toddler, and child. This person is not having a bunch of training data blasted at them. They’re gradually learning about the world in an interactive, multi-sensory and multi-manipulative manner. The true understanding of words and concepts comes from integrating all of their senses with their own manipulations as well as feedback from their parents.
Children also are not blank slates, as is popularly claimed, but come equipped with built-in brain structures for vision, including facial recognition, voice recognition (the ability to recognize mom’s voice within a day or two of birth), universal grammar, and a program for learning motor coordination through sensory feedback.
[Edit: but to be clear, for a pretrained model this probability means "what's my estimate of the conditional probability of this token occurring in the pretraining dataset?", not "how likely is this statement to be true?" And for a post-trained model, the probability really has no simple interpretation other than "this is the probability that I will output this token in this situation".]
Basically, you’d need a lot more computing power to come up with a distribution of the output of an LLM than to come up with a single answer.
But the model "shape" and computation graph itself doesn't change as a result of post-training. All that changes is the weights in the matrices.
You never see this in the response but you do in the reasoning.
- How aligned has it been to “know” that something is true (eg ethical constraints)
- Statistical significance and just being able to corroborate one alternative in Its training data more strongly than another
- If it’s a web search related query, is the statement from original sources vs synthesised from say third party sources
But I’m just a layman and could be totally off here.
E.g. getting two r's in strawberry could very well have a very high "confidence score" while a random but rare correct fact might have a very well a very low one.
In short: LLM have no concept, or even desire to produce of truth
They do produce true statements most of the time, though.
Try to explain why one shotting works.
If you train an LLM on mostly false statements, it will generate both known and novel falsehoods. Same for truth.
An LLM has no intrinsic concept of true or false, everything is a function of the training set. It just generates statements similar to what it has seen and higher-dimensional analogies of those .