@inproceedings{bazarova-etal-2026-hallucination,
title = "Hallucination Detection in {LLM}s with Topological Divergence on Attention Graphs",
author = "Bazarova, Alexandra and
Volodichev, Andrei and
Yugay, Aleksandr and
Shulga, Andrey and
Ermilova, Alina and
Polev, Konstantin and
Belikova, Julia and
Parchiev, Rauf and
Simakov, Dmitry and
Savchenko, Maxim and
Savchenko, Andrey and
Barannikov, Serguei and
Zaytsev, Alexey",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-long.704/",
pages = "15449--15470",
ISBN = "979-8-89176-390-6",
abstract = "Hallucinations remain a critical challenge for large language models (LLMs), particularly in Retrieval-Augmented Generation (RAG) settings where models may generate outputs unsupported by the provided context. To address this, we introduce TOHA, a TOpology-based HAllucination detector, which leverages a topological divergence metric to quantify the structural properties of graphs induced by attention matrices. Examining the topological divergence between prompt and response subgraphs in RAG settings reveals consistent patterns: higher divergence values in specific attention heads correlate with unfaithful outputs, independent of the dataset. Extensive experiments {---} including evaluations on question answering and summarization tasks {---} show that our approach achieves state-of-the-art or competitive results on several benchmarks while requiring minimal annotated data and computational resources. Our findings indicate that the topological structure of attention matrices provides an efficient and robust metric for assessing the correctness of LLM{'}s responses."
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<abstract>Hallucinations remain a critical challenge for large language models (LLMs), particularly in Retrieval-Augmented Generation (RAG) settings where models may generate outputs unsupported by the provided context. To address this, we introduce TOHA, a TOpology-based HAllucination detector, which leverages a topological divergence metric to quantify the structural properties of graphs induced by attention matrices. Examining the topological divergence between prompt and response subgraphs in RAG settings reveals consistent patterns: higher divergence values in specific attention heads correlate with unfaithful outputs, independent of the dataset. Extensive experiments — including evaluations on question answering and summarization tasks — show that our approach achieves state-of-the-art or competitive results on several benchmarks while requiring minimal annotated data and computational resources. Our findings indicate that the topological structure of attention matrices provides an efficient and robust metric for assessing the correctness of LLM’s responses.</abstract>
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%0 Conference Proceedings
%T Hallucination Detection in LLMs with Topological Divergence on Attention Graphs
%A Bazarova, Alexandra
%A Volodichev, Andrei
%A Yugay, Aleksandr
%A Shulga, Andrey
%A Ermilova, Alina
%A Polev, Konstantin
%A Belikova, Julia
%A Parchiev, Rauf
%A Simakov, Dmitry
%A Savchenko, Maxim
%A Savchenko, Andrey
%A Barannikov, Serguei
%A Zaytsev, Alexey
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F bazarova-etal-2026-hallucination
%X Hallucinations remain a critical challenge for large language models (LLMs), particularly in Retrieval-Augmented Generation (RAG) settings where models may generate outputs unsupported by the provided context. To address this, we introduce TOHA, a TOpology-based HAllucination detector, which leverages a topological divergence metric to quantify the structural properties of graphs induced by attention matrices. Examining the topological divergence between prompt and response subgraphs in RAG settings reveals consistent patterns: higher divergence values in specific attention heads correlate with unfaithful outputs, independent of the dataset. Extensive experiments — including evaluations on question answering and summarization tasks — show that our approach achieves state-of-the-art or competitive results on several benchmarks while requiring minimal annotated data and computational resources. Our findings indicate that the topological structure of attention matrices provides an efficient and robust metric for assessing the correctness of LLM’s responses.
%U https://aclanthology.org/2026.acl-long.704/
%P 15449-15470
Markdown (Informal)
[Hallucination Detection in LLMs with Topological Divergence on Attention Graphs](https://aclanthology.org/2026.acl-long.704/) (Bazarova et al., ACL 2026)
ACL
- Alexandra Bazarova, Andrei Volodichev, Aleksandr Yugay, Andrey Shulga, Alina Ermilova, Konstantin Polev, Julia Belikova, Rauf Parchiev, Dmitry Simakov, Maxim Savchenko, Andrey Savchenko, Serguei Barannikov, and Alexey Zaytsev. 2026. Hallucination Detection in LLMs with Topological Divergence on Attention Graphs. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 15449–15470, San Diego, California, United States. Association for Computational Linguistics.