@inproceedings{binkowski-etal-2025-hallucination,
title = "Hallucination Detection in {LLM}s Using Spectral Features of Attention Maps",
author = "Binkowski, Jakub and
Janiak, Denis and
Sawczyn, Albert and
Gabrys, Bogdan and
Kajdanowicz, Tomasz Jan",
editor = "Christodoulopoulos, Christos and
Chakraborty, Tanmoy and
Rose, Carolyn and
Peng, Violet",
booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.emnlp-main.1239/",
doi = "10.18653/v1/2025.emnlp-main.1239",
pages = "24354--24385",
ISBN = "979-8-89176-332-6",
abstract = "Large Language Models (LLMs) have demonstrated remarkable performance across various tasks but remain prone to hallucinations. Detecting hallucinations is essential for safety-critical 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 utilises the top-$k$ eigenvalues of the Laplacian matrix derived from the attention maps as an input to hallucination detection probes. Empirical evaluations demonstrate that our approach achieves state-of-the-art hallucination detection performance among attention-based methods. Extensive ablation studies further highlight the robustness and generalisation of LapEigvals, paving the way for future advancements in the hallucination detection domain."
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<abstract>Large Language Models (LLMs) have demonstrated remarkable performance across various tasks but remain prone to hallucinations. Detecting hallucinations is essential for safety-critical 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 utilises the top-k eigenvalues of the Laplacian matrix derived from the attention maps as an input to hallucination detection probes. Empirical evaluations demonstrate that our approach achieves state-of-the-art hallucination detection performance among attention-based methods. Extensive ablation studies further highlight the robustness and generalisation of LapEigvals, paving the way for future advancements in the hallucination detection domain.</abstract>
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%0 Conference Proceedings
%T Hallucination Detection in LLMs Using Spectral Features of Attention Maps
%A Binkowski, Jakub
%A Janiak, Denis
%A Sawczyn, Albert
%A Gabrys, Bogdan
%A Kajdanowicz, Tomasz Jan
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-332-6
%F binkowski-etal-2025-hallucination
%X Large Language Models (LLMs) have demonstrated remarkable performance across various tasks but remain prone to hallucinations. Detecting hallucinations is essential for safety-critical 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 utilises the top-k eigenvalues of the Laplacian matrix derived from the attention maps as an input to hallucination detection probes. Empirical evaluations demonstrate that our approach achieves state-of-the-art hallucination detection performance among attention-based methods. Extensive ablation studies further highlight the robustness and generalisation of LapEigvals, paving the way for future advancements in the hallucination detection domain.
%R 10.18653/v1/2025.emnlp-main.1239
%U https://aclanthology.org/2025.emnlp-main.1239/
%U https://doi.org/10.18653/v1/2025.emnlp-main.1239
%P 24354-24385
Markdown (Informal)
[Hallucination Detection in LLMs Using Spectral Features of Attention Maps](https://aclanthology.org/2025.emnlp-main.1239/) (Binkowski et al., EMNLP 2025)
ACL