Attention Dispersion as a Diagnostic Signal for Hallucination in Large Language Models
Large Language Models (LLMs) frequently exhibit hallucinations, presenting a major barrier to reliability in complex reasoning tasks. While traditional detection methods rely on output-based confidence metrics, these logits are often miscalibrated by modern alignment techniques. In this paper, we investigate the temporal volatility of internal attention mechanisms as an alternative diagnostic signal for hallucination that does not depend on output calibration. By introducing an unsupervised metric for attention dispersion, we show that epistemic uncertainty leaves a measurable trace within intermediate layers, where spikes in attention entropy are associated with reasoning breakdowns. We ...
| Authors | Shardul P. More, Tanuja S. Pawar |
|---|---|
| Primary category | cs.CL |
| Published | 16 September 2026 |
| Last updated at source | 16 September 2026 |
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