@inproceedings{liu-etal-2025-towards,
title = "Towards Long Context Hallucination Detection",
author = "Liu, Siyi and
Halder, Kishaloy and
Qi, Zheng and
Xiao, Wei and
Pappas, Nikolaos and
Htut, Phu Mon and
Anna John, Neha and
Benajiba, Yassine and
Roth, Dan",
editor = "Chiruzzo, Luis and
Ritter, Alan and
Wang, Lu",
booktitle = "Findings of the Association for Computational Linguistics: NAACL 2025",
month = apr,
year = "2025",
address = "Albuquerque, New Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-naacl.436/",
doi = "10.18653/v1/2025.findings-naacl.436",
pages = "7827--7835",
ISBN = "979-8-89176-195-7",
abstract = "Large Language Models (LLMs) have demonstrated remarkable performance across various tasks. However, they are prone to contextual hallucination, generating information that is either unsubstantiated or contradictory to the given context. Although many studies have investigated contextual hallucinations in LLMs, addressing them in long-context inputs remains an open problem. In this work, we take an initial step toward solving this problem by constructing a dataset specifically designed for long-context hallucination detection. Furthermore, we propose a novel architecture that enables pre-trained encoder models, such as BERT, to process long contexts and effectively detect contextual hallucinations through a decomposition and aggregation mechanism. Our experimental results show that the proposed architecture significantly outperforms previous models of similar size as well as LLM-based models across various metrics, while providing substantially faster inference. We publicly release our dataset and code to promote research along the same line."
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<abstract>Large Language Models (LLMs) have demonstrated remarkable performance across various tasks. However, they are prone to contextual hallucination, generating information that is either unsubstantiated or contradictory to the given context. Although many studies have investigated contextual hallucinations in LLMs, addressing them in long-context inputs remains an open problem. In this work, we take an initial step toward solving this problem by constructing a dataset specifically designed for long-context hallucination detection. Furthermore, we propose a novel architecture that enables pre-trained encoder models, such as BERT, to process long contexts and effectively detect contextual hallucinations through a decomposition and aggregation mechanism. Our experimental results show that the proposed architecture significantly outperforms previous models of similar size as well as LLM-based models across various metrics, while providing substantially faster inference. We publicly release our dataset and code to promote research along the same line.</abstract>
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%0 Conference Proceedings
%T Towards Long Context Hallucination Detection
%A Liu, Siyi
%A Halder, Kishaloy
%A Qi, Zheng
%A Xiao, Wei
%A Pappas, Nikolaos
%A Htut, Phu Mon
%A Anna John, Neha
%A Benajiba, Yassine
%A Roth, Dan
%Y Chiruzzo, Luis
%Y Ritter, Alan
%Y Wang, Lu
%S Findings of the Association for Computational Linguistics: NAACL 2025
%D 2025
%8 April
%I Association for Computational Linguistics
%C Albuquerque, New Mexico
%@ 979-8-89176-195-7
%F liu-etal-2025-towards
%X Large Language Models (LLMs) have demonstrated remarkable performance across various tasks. However, they are prone to contextual hallucination, generating information that is either unsubstantiated or contradictory to the given context. Although many studies have investigated contextual hallucinations in LLMs, addressing them in long-context inputs remains an open problem. In this work, we take an initial step toward solving this problem by constructing a dataset specifically designed for long-context hallucination detection. Furthermore, we propose a novel architecture that enables pre-trained encoder models, such as BERT, to process long contexts and effectively detect contextual hallucinations through a decomposition and aggregation mechanism. Our experimental results show that the proposed architecture significantly outperforms previous models of similar size as well as LLM-based models across various metrics, while providing substantially faster inference. We publicly release our dataset and code to promote research along the same line.
%R 10.18653/v1/2025.findings-naacl.436
%U https://aclanthology.org/2025.findings-naacl.436/
%U https://doi.org/10.18653/v1/2025.findings-naacl.436
%P 7827-7835
Markdown (Informal)
[Towards Long Context Hallucination Detection](https://aclanthology.org/2025.findings-naacl.436/) (Liu et al., Findings 2025)
ACL
- Siyi Liu, Kishaloy Halder, Zheng Qi, Wei Xiao, Nikolaos Pappas, Phu Mon Htut, Neha Anna John, Yassine Benajiba, and Dan Roth. 2025. Towards Long Context Hallucination Detection. In Findings of the Association for Computational Linguistics: NAACL 2025, pages 7827–7835, Albuquerque, New Mexico. Association for Computational Linguistics.