AURORA: Asymmetry and Update-Induced Rotation for Robust Hallucination Detection in Large Language Models
Large Language Models (LLMs) have demonstrated remarkable capabilities across a wide range of natural language processing tasks. However, their tendency to generate hallucinations, namely factually incorrect or unfaithful outputs, poses a critical obstacle to their deployment in high-stakes applications. Although recent hallucination detection methods have made encouraging progress, they typically rely on costly output-level consistency checks or static hidden-state probes that capture shallow dataset-specific patterns, leading to substantial degradation under cross-dataset evaluation. In this work, we propose AURORA, a novel hallucination detection framework that shifts the focus from st...
| Authors | Zishuai Zhang, Hainan Zhang, Zhiming Zheng |
|---|---|
| Primary category | cs.CL |
| Published | 28 June 2026 |
| Last updated at source | 28 June 2026 |
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