@article{zhang-etal-2025-sirens,
    title = "🧜{S}iren{'}s Song in the {AI} Ocean: A Survey on Hallucination in Large Language Models",
    author = "Zhang, Yue  and
      Li, Yafu  and
      Cui, Leyang  and
      Cai, Deng  and
      Liu, Lemao  and
      Fu, Tingchen  and
      Huang, Xinting  and
      Zhao, Enbo  and
      Zhang, Yu  and
      Chen, Yulong  and
      Wang, Longyue  and
      Luu, Anh Tuan  and
      Bi, Wei  and
      Shi, Freda  and
      Shi, Shuming",
    journal = "Computational Linguistics",
    volume = "51",
    number = "4",
    month = dec,
    year = "2025",
    address = "Cambridge, MA",
    publisher = "MIT Press",
    url = "https://preview.aclanthology.org/ingest-eacl/2025.cl-4.9/",
    doi = "10.1162/coli.a.16",
    pages = "1373--1418",
    abstract = "While large language models (LLMs) have demonstrated remarkable capabilities across a range of downstream tasks, a significant concern revolves around their propensity to exhibit hallucinations: LLMs occasionally generate content that diverges from the user input, contradicts previously generated context, or misaligns with established world knowledge. This phenomenon poses a substantial challenge to the reliability of LLMs in real-world scenarios. In this article, we survey recent efforts on the detection, explanation, and mitigation of hallucination, with an emphasis on the unique challenges posed by LLMs. We present taxonomies of the LLM hallucination phenomena and evaluation benchmarks, analyze existing approaches aiming at mitigating LLM hallucination, and discuss potential directions for future research."
}