A Probabilistic Framework for LLM Hallucination Detection via Belief Tree Propagation
Bairu Hou, Yang Zhang, Jacob Andreas, Shiyu Chang
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.
BibTeX
@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://aclanthology.org/2025.naacl-long.158/",
pages = "3076--3099",
ISBN = "979-8-89176-189-6"
}