@inproceedings{li-etal-2025-hd,
    title = "{HD}-{NDE}s: Neural Differential Equations for Hallucination Detection in {LLM}s",
    author = "Li, Qing  and
      Geng, Jiahui  and
      Chen, Zongxiong  and
      Zhu, Derui  and
      Wang, Yuxia  and
      Ma, Congbo  and
      Lyu, Chenyang  and
      Karray, Fakhri",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingestion-acl-25/2025.acl-long.309/",
    pages = "6173--6186",
    ISBN = "979-8-89176-251-0",
    abstract = "In recent years, large language models (LLMs) have made remarkable advancements, yet hallucination, where models produce inaccurate or non-factual statements, remains a significant challenge for real-world deployment. Although current classification-based methods, such as SAPLMA, are highly efficient in mitigating hallucinations, they struggle when non-factual information arises in the early or mid-sequence of outputs, reducing their reliability. To address these issues, we propose Hallucination Detection-Neural Differential Equations (HD-NDEs), a novel method that systematically assesses the truthfulness of statements by capturing the full dynamics of LLMs within their latent space. Our approaches apply neural differential equations (Neural DEs) to model the dynamic system in the latent space of LLMs. Then, the sequence in the latent space is mapped to the classification space for truth assessment. The extensive experiments across five datasets and six widely used LLMs demonstrate the effectiveness of HD-NDEs, especially, achieving over 14{\%} improvement in AUC-ROC on the True-False dataset compared to state-of-the-art techniques."
}