Calibrating Verbal Uncertainty as a Linear Feature to Reduce Hallucinations

• URL: http://arxiv.org/abs/2503.14477v2
• Date: Tue, 22 Apr 2025 19:00:41 GMT
• Title: Calibrating Verbal Uncertainty as a Linear Feature to Reduce Hallucinations
• Authors: Ziwei Ji, Lei Yu, Yeskendir Koishekenov, Yejin Bang, Anthony Hartshorn, Alan Schelten, Cheng Zhang, Pascale Fung, Nicola Cancedda,
• Abstract summary: We find that verbal uncertainty'' is governed by a single linear feature in the representation space of LLMs.<n>We show that this has only moderate correlation with the actual semantic uncertainty'' of the model.
• Score: 51.92795774118647
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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%.

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