Hallucination Detection in LLMs Using Spectral Features of Attention Maps

Large Language Models (LLMs) have demonstrated remarkable performance across various tasks but remain prone to hallucinations. Detecting hallucinations is essential for safetycritical applications, and recent methods leverage attention map properties to this end, though their effectiveness remains limited. In this work, we investigate the spectral features of attention maps by interpreting them as adjacency matrices of graph structures. We propose the LapEigvals method, which utilizes the topk eigenvalues of the Laplacian matrix derived from the attention maps as an input to hallucination detection probes. Empirical evaluations demonstrate that our approach achieves stateof-the-art hallucination detection performance among attention-based methods. Extensive ablation studies further highlight the robustness and generalization of LapEigvals, paving the way for future advancements in the hallucination detection domain.