@inproceedings{li-etal-2026-hallucination,
    title = "Hallucination Detection in Long-Form Text Generated by {LLM}s: A Benchmark and a Hyper-Relational Knowledge Graph Approach",
    author = "Li, Zituo  and
      Chen, Guangzhou  and
      Sun, Jianbin  and
      Song, Qi  and
      Kewei, Yang",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.findings-acl.1673/",
    pages = "33477--33494",
    ISBN = "979-8-89176-395-1",
    abstract = "Hallucination detection has attracted increasing attention, particularly in long-form text generation, where language models are more prone to producing factually inaccurate content. Prior studies reveal two limitations: (1) current benchmarks focus on short-form content, lacking the structural complexity required in long-form scenarios; (2) existing methods are constrained by coarse-grained consistency checks and fail to capture long-range and hyper-relational dependencies. To address these challenges, we provide LHD, a benchmark for long-form hallucination detection that contains diverse entity types and intricate factual dependencies spanning extended contexts. We further propose HRKG-HD, a zero-resource, black-box framework that models responses as fact-centric hyper-relational knowledge graphs and detects hallucinations through relation-aware multi-hop reasoning over these graphs. By linking distant facts through shared entities and qualifiers, this design enables a global and dependency-aware verification of factual consistency. Extensive experiments demonstrate that HRKG-HD not only outperforms existing baselines but also exhibits robust and consistent performance across various LLMs."
}