用大模型生成伪标签，再通过全局传播提升假新闻检测效果。
Synergizing LLMs with Global Label Propagation for Multimodal Fake News Detection
- 结合大模型伪标签与全局标签传播机制
- 在基准数据集上超越现有最先进方法
- 适合关注多模态假新闻检测的研究者
大型语言模型（LLMs）可通过预测伪标签辅助多模态假新闻检测，但仅依赖其生成的伪标签性能较差，难以有效整合。本文提出基于大模型伪标签的全局标签传播网络（GLPN-LLM），通过标签传播技术融合LLM能力。全局标签传播利用伪标签信息，在所有样本间传递标签信息以提升预测准确率。为防止训练过程中标签泄露，设计了掩码机制，确保训练节点不会将自身标签回传。在多个基准数据集上的实验表明，通过协同大模型与标签传播，本模型显著优于现有最先进方法。
原文摘要 · Abstract (English)
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.
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