LLMs often adopt an assertive language style also when making false claims. Such ``overconfident hallucinations'' mislead users and erode trust. Achieving the ability to express in language the actual degree of uncertainty around a claim is therefore of great importance. We find that ``verbal uncertainty'' is governed by a single linear feature in the representation space of LLMs, and show that this has only moderate correlation with the actual ``semantic uncertainty'' of the model. We apply this insight and show that (1) the mismatch between semantic and verbal uncertainty is a better predictor of hallucinations than semantic uncertainty alone and (2) we can intervene on verbal uncertainty at inference time and reduce confident hallucinations on short-form answers, achieving an average relative reduction of ~30%.
@article{arxiv.2503.14477,
title = {Calibrating Verbal Uncertainty as a Linear Feature to Reduce Hallucinations},
author = {Ziwei Ji and Lei Yu and Yeskendir Koishekenov and Yejin Bang and Anthony Hartshorn and Alan Schelten and Cheng Zhang and Pascale Fung and Nicola Cancedda},
journal= {arXiv preprint arXiv:2503.14477},
year = {2025}
}