Don't ask an LLM for a confidence score

I agree with the author if we are trying to use this as some sort of absolute scale of confidence. It only develops meaning when we control for many other variables. Looking at confidence scores across two different models or prompts is probably not a good idea.

There was a post earlier on HN where they trained a probe which could give a realistic confidence score on an LLM, they claim with 81% accuracy. They used it to interrupt and switch to a smarter model if a dumber one had low confidence: https://news.ycombinator.com/item?id=49010782

> the capability is highly unreliable and highly context-dependent.

Sounds like what's highly unreliable is the evidence, making the "capability" just another imagining.

Who can read that text color and background combo? I had to turn on reader mode in Firefox.

So I agree with the general geist of this, a few counter-examples though:

If you have the raw log-probs, I show how to use conformal inference to set false-positive or recall rates, https://crimede-coder.com/blogposts/2026/ConfClassification

Some of the peer reviewed papers with the older models did show calibration was bad (not close to monotonic). This post with newer models (for one example, classifiying injuries) is not that bad, https://gmcirco.github.io/blog/posts/ai-calibration/calibrat....

I mean it just depends on the application, what level of error you can consider. But the second post shows how to recalibrate the scores as well if you need calibrated probabilities.