A Probabilistic Framework for LLM Hallucination Detection via Belief Tree Propagation

TL;DR

This paper introduces BTProp, a probabilistic framework that uses belief trees and hidden Markov models to improve hallucination detection in large language models, outperforming existing methods.

Contribution

The paper proposes a novel belief tree propagation framework that effectively integrates continuous belief scores for better hallucination detection in LLMs.

Findings

BTProp improves detection performance by 3%-9% on benchmarks.

The belief tree structure captures logical relations among statements.

Continuous belief integration enhances detection accuracy.

Abstract

This paper focuses on the task of hallucination detection, which aims to determine the truthfulness of LLM-generated statements. To address this problem, a popular class of methods utilize the LLM's self-consistencies in its beliefs in a set of logically related augmented statements generated by the LLM, which does not require external knowledge databases and can work with both white-box and black-box LLMs. However, in many existing approaches, the augmented statements tend to be very monotone and unstructured, which makes it difficult to integrate meaningful information from the LLM beliefs in these statements. Also, many methods work with the binarized version of the LLM's belief, instead of the continuous version, which significantly loses information. To overcome these limitations, in this paper, we propose Belief Tree Propagation (BTProp), a probabilistic framework for LLM hallucination detection. BTProp introduces a belief tree of logically related statements by recursively decomposing a parent statement into child statements with three decomposition strategies, and builds a hidden Markov tree model to integrate the LLM's belief scores in these statements in a principled way. Experiment results show that our method improves baselines by 3%-9% (evaluated by AUROC and AUC-PR) on multiple hallucination detection benchmarks. Code is available at https://github.com/UCSB-NLP-Chang/BTProp.