@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://preview.aclanthology.org/fix-sig-urls/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."
}