@inproceedings{hou-etal-2025-probabilistic,
    title = "A Probabilistic Framework for {LLM} Hallucination Detection via Belief Tree Propagation",
    author = "Hou, Bairu  and
      Zhang, Yang  and
      Andreas, Jacob  and
      Chang, Shiyu",
    editor = "Chiruzzo, Luis  and
      Ritter, Alan  and
      Wang, Lu",
    booktitle = "Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)",
    month = apr,
    year = "2025",
    address = "Albuquerque, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/moar-dois/2025.naacl-long.158/",
    doi = "10.18653/v1/2025.naacl-long.158",
    pages = "3076--3099",
    ISBN = "979-8-89176-189-6",
    abstract = "We describe Belief Tree Propagation (BTProp), a probabilistic framework for LLM hallucination detection. To judge the truth of a statement, BTProp generates a belief tree by recursively expanding the initial statement into a set of logically related claims, then reasoning globally about the relationships between these claims. BTProp works by constructing a probabilistic model of the LM itself: it reasons jointly about logical relationships between claims and relationships between claim probabilities and LM factuality judgments via probabilistic inference in a ``hidden Markov tree''. This method improves over state-of-the-art baselines by 3{\%}-9{\%} (evaluated by AUROC and AUC-PR) on multiple hallucination detection benchmarks."
}