Detecting Hallucinations in Large Language Models via Internal Attention Divergence Signals
We propose a lightweight and single-pass uncertainty quantification method for detecting hallucinations in Large Language Models. The method uses attention matrices to estimate uncertainty without requiring repeated sampling or external models. Specifically, we measure the Kullback-Leibler divergence between each attention head's distribution and a uniform reference distribution, and use these features in a logistic regression probe. Across multiple datasets, task types, and model families, attention divergence is highly predictive of answer correctness and performs competitively with existing uncertainty estimation methods. We find that this signal is concentrated in middle layers and on...
| Authors | Gijs van Dijk |
|---|---|
| Primary category | cs.CL |
| Published | 6 May 2026 |
| Last updated at source | 6 May 2026 |
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