I’ve run those experiments. Commercially, so I can’t share data. But I can say what the article describes roughly matches what I’ve seen. How well calibrated the scores are varies by model, application, and subtle wording changes. I’m a little more optimistic than the author in that I have gotten it to work reasonably well. But I would not recommend productizing it with a SaaS LLM because it will break every time your vendor sunsets the model you were using, and getting it working again on a new model will be an unholy PITA.

I would guess it tends towards more likely because it can't not think of something mentioned.

But I agree, I would8 much rather have some data than reckons.

It's with noting that people do this too. They tend to vastly overetimate a low probability event that they know is possible. Often by more than one order of magnitude.

It is something worth keeping in mind when you are people throwing around personal p doom scores.

Interestingly when humans have to make a decision based upon the probability they feel, they tend to act far closer to the actual probability than what they would say the probability is.

You mean like this recent preprint?

https://arxiv.org/html/2607.20526v1

From that paper, model accuracy is not perfect, but it's overall pretty favourable. There are some encouraging findings in there, too, including that accuracy of model confidence scales with reasoning effort.