#CEOs starting to find #AI productivity gains ‘underwhelming,’ -
https://kensbookinfo.blogspot.com/p/etc.html#Canada

5 Common #Wi-Fi Mistakes Far Too Many People Make -
https://kensbookinfo.blogspot.com/p/infotech.html#85

13 Best Monitor #Calibration Tools for 2026 -
https://kensbookinfo.blogspot.com/p/infotech.html#55

MP warns of #Israeli “takeover” of island through land -
https://kensbookinfo.blogspot.com/p/uk.html#bbcwales

#World's Fastest Date -
https://kensbookinfo.blogspot.com/p/etc.html#Jordan

View all news from Capitals in the US https://kensbookinfo.blogspot.com/2026/03/latest-news-from-capitals-in-united.html

Another reason why I prefer b&w #photography? Both are calibrated(!) #BenQ monitors (albeit different models). According to the measuring device, both are supposedly perfectly calibrated and yet they look that bloody different ... 🤯. Oh, ffs!

#calibration #i1Profiler #Xrite

How do we make LLM output more trustworthy? A short survey note on three lines of recent work covering five papers: conformal-prediction coverage guarantees, behavioral calibration of the model's prose, and sample-disagreement detection. All three pay the same multi-sample inference tax; the choice is about what you want back.

https://benjaminhan.net/posts/20260505-llm-uncertainty-survey/?utm_source=mastodon&utm_medium=social

#Hallucination #LLMs #Calibration #ConformalPrediction #AI

Semantic Entropy (Nature 2024) detects LLM confabulations by clustering sampled answers by meaning and computing entropy over the cluster distribution. "Paris" and "It's Paris" cluster together, so paraphrase noise doesn't inflate the signal. Cost: it only catches hallucinations that vary across samples. If the model is consistently wrong, all samples cluster and the detector says "confident".

https://benjaminhan.net/posts/20260505-semantic-entropy/?utm_source=mastodon&utm_medium=social

#LLMs #Calibration #Hallucination #Nature #AI

Linguistic Calibration trains Llama 2 to emit confidence phrases that let a downstream reader make calibrated forecasts on related questions. The key move is defining calibration through reader utility instead of self-reported probability. Hedged text that doesn't help the reader makes no forecasting progress, so generic hedging can't game the objective.

https://benjaminhan.net/posts/20260505-linguistic-calibration/?utm_source=mastodon&utm_medium=social

#LLMs #Calibration #Hallucination #ICML #AI

Conformal Factuality casts LM correctness as uncertainty quantification. Decompose the answer into sub-claims, score each, drop the low-confidence ones until the retained set is ~1-α factual. The sub-claim decomposition is doing most of the work, and the conformal machinery rides on top. Atomic-claim splitters have known failure modes, and the guarantee inherits them.

https://benjaminhan.net/posts/20260505-conformal-factuality/?utm_source=mastodon&utm_medium=social

#ConformalPrediction #Calibration #Hallucination #LLMs #ICML #AI

A primer on conformal prediction: the recipe for distribution-free coverage guarantees that doesn't require your model to be calibrated. Rank-based non-conformity scores plus a calibration quantile give you valid prediction sets. Easy inputs get one-class sets; hard ones get many alternatives. Set size is where the uncertainty shows up.

https://benjaminhan.net/posts/20260505-conformal-prediction-primer/?utm_source=mastodon&utm_medium=social

#ConformalPrediction #Calibration #UncertaintyQuantification #AI

Scale buys calibration in exactly the format you tested in. Lettered MC works at 52B; swap one option for "none of the above" and it collapses; True/False rephrasing restores it. RLHF policies need a temperature fix. "Do I know this?" heads miscalibrate out of distribution. Three years on, SelfReflect lands the same conclusion: only sampling your own answers and summarizing gets an LLM to describe its own distribution.

https://benjaminhan.net/posts/20260504-kadavath-mostly-know/?utm_source=mastodon&utm_medium=social

#LLMs #Calibration #AISafety #Anthropic #AI