Logical Consistency as a Bridge: Improving LLM Hallucination Detection via Label Constraint Modeling between Responses and Self-Judgments

TL;DR

LaaB introduces a novel framework that enhances hallucination detection in LLMs by integrating neural features and symbolic judgments through logical consistency, improving reliability across multiple datasets and models.

Contribution

It proposes a meta-judgment process that maps symbolic labels into feature space, exploiting the logical relationship between responses and self-judgments for better hallucination detection.

Findings

LaaB outperforms 8 baselines on 4 datasets and models.

The framework effectively leverages logical consistency to improve detection accuracy.

Extensive experiments validate the superiority of LaaB across diverse settings.

Abstract

Large Language Models (LLMs) are prone to factual hallucinations, risking their reliability in real-world applications. Existing hallucination detectors mainly extract micro-level intrinsic patterns for uncertainty quantification or elicit macro-level self-judgments through verbalized prompts. However, these methods address only a single facet of the hallucination, focusing either on implicit neural uncertainty or explicit symbolic reasoning, thereby treating these inherently coupled behaviors in isolation and failing to exploit their interdependence for a holistic view. In this paper, we propose LaaB (Logical Consistency-as-a-Bridge), a framework that bridges neural features and symbolic judgments for hallucination detection. LaaB introduces a "meta-judgment" process to map symbolic labels back into the feature space. By leveraging the inherent logical bridge where response and meta-judgment labels are either the same or opposite based on the self-judgment's semantics, LaaB aligns and integrates dual-view signals via mutual learning and enhances the hallucination detection. Extensive experiments on 4 public datasets, across 4 LLMs, against 8 baselines demonstrate the superiority of LaaB.