Detecting Contextual Hallucinations in LLMs with Frequency-Aware Attention

• URL: http://arxiv.org/abs/2602.18145v1
• Date: Fri, 20 Feb 2026 11:18:45 GMT
• Title: Detecting Contextual Hallucinations in LLMs with Frequency-Aware Attention
• Authors: Siya Qi, Yudong Chen, Runcong Zhao, Qinglin Zhu, Zhanghao Hu, Wei Liu, Yulan He, Zheng Yuan, Lin Gui,
• Abstract summary: We introduce a frequency-aware perspective on attention by analyzing its variation during generation.<n>We develop a lightweight hallucination detector using high-frequency attention features.
• Score: 27.49425252327799
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Hallucination detection is critical for ensuring the reliability of large language models (LLMs) in context-based generation. Prior work has explored intrinsic signals available during generation, among which attention offers a direct view of grounding behavior. However, existing approaches typically rely on coarse summaries that fail to capture fine-grained instabilities in attention. Inspired by signal processing, we introduce a frequency-aware perspective on attention by analyzing its variation during generation. We model attention distributions as discrete signals and extract high-frequency components that reflect rapid local changes in attention. Our analysis reveals that hallucinated tokens are associated with high-frequency attention energy, reflecting fragmented and unstable grounding behavior. Based on this insight, we develop a lightweight hallucination detector using high-frequency attention features. Experiments on the RAGTruth and HalluRAG benchmarks show that our approach achieves performance gains over verification-based, internal-representation-based, and attention-based methods across models and tasks.

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