Attention Sinks as Internal Signals for Hallucination Detection in Large Language Models
Large language models frequently exhibit hallucinations: fluent and confident outputs that are factually incorrect or unsupported by the input context. While recent hallucination detection methods have explored various features derived from attention maps, the underlying mechanisms they exploit remain poorly understood. In this work, we propose SinkProbe, a hallucination detection method grounded in the observation that hallucinations are deeply entangled with attention sinks - tokens that accumulate disproportionate attention mass during generation - indicating a transition from distributed, input-grounded attention to compressed, prior-dominated computation. Importantly, although sink s...
| Authors | Jakub Binkowski, Kamil Adamczewski, Tomasz Kajdanowicz |
|---|---|
| Primary category | cs.CL |
| Published | 12 April 2026 |
| Last updated at source | 1 August 2026 |
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