[dmrivers]

The statement near the top of the post > "The short version: asking an LLM to generate a score for how confident it is in its own response is, from everything I can tell, completely useless."

is definitely too strong of a claim and directly undercut by what is said near the end of the post: > "Tian et al. found in Just Ask for Calibration that with the right prompting strategy, RLHF’d models verbalize probabilities that are better calibrated than the model’s own conditional probabilities, and that prompting plus temperature scaling can cut expected calibration error by more than half. And Anthropic’s Language Models (Mostly) Know What They Know found encouraging results asking models to estimate the probability that their own proposed answer is true."

My own experience is that stated confidence is a helpful tool and of course you need a rubric and a proper prompt, but this is clearly less work than training a classifier (as advocated by the post) and requires less data.

[StellaSearch]

Fully agree. I found for most of my work with LLMs and Finance, ~90% of the time a high or low confidence score was accurate. There's the occasional ambiguous case, and that'll happen, but the engineering work that comes with building a classifier makes it not practical for my usecases.

[inspectorSlap]

I'm working on a universal one. My belief is that as agentic capabilities increase, behavioral attribution will be increasingly needed to maintain quality.

[inspectorSlap]

Agreed. However my research is showing that saturation of the rubric occurs in unique configuration that drive the transfer curve to near binary. Effectively turning a 1-10 rating system into yes/no.