Synergizing LLMs with Global Label Propagation for Multimodal Fake News Detection

TL;DR

This paper introduces GLPN-LLM, a novel method that combines Large Language Models with global label propagation to improve multimodal fake news detection accuracy, effectively leveraging LLM pseudo labels.

Contribution

The paper presents a new framework integrating LLMs with label propagation, including a mask-based mechanism to prevent label leakage, leading to improved detection performance.

Findings

Achieves superior accuracy over state-of-the-art baselines

Effective utilization of LLM-generated pseudo labels

Demonstrates robustness across benchmark datasets

Abstract

Large Language Models (LLMs) can assist multimodal fake news detection by predicting pseudo labels. However, LLM-generated pseudo labels alone demonstrate poor performance compared to traditional detection methods, making their effective integration non-trivial. In this paper, we propose Global Label Propagation Network with LLM-based Pseudo Labeling (GLPN-LLM) for multimodal fake news detection, which integrates LLM capabilities via label propagation techniques. The global label propagation can utilize LLM-generated pseudo labels, enhancing prediction accuracy by propagating label information among all samples. For label propagation, a mask-based mechanism is designed to prevent label leakage during training by ensuring that training nodes do not propagate their own labels back to themselves. Experimental results on benchmark datasets show that by synergizing LLMs with label propagation, our model achieves superior performance over state-of-the-art baselines.