用大模型指导多视角推理，高效提升假新闻识别准确率
LLM-MRD: LLM-Guided Multi-View Reasoning Distillation for Fake News Detection
- 用大模型生成多模态推理链，指导学生模型学习
- 在多个数据集上准确率提升5.19%，假新闻检测F1值提升6.33%
- 适合需要高效且精准假新闻检测的场景
多模态假新闻检测对遏制社会虚假信息至关重要。现有方法通过融合多模态特征或利用大语言模型（LLMs）进行深度推理来应对，但存在多视角判断不完整、融合不充分，以及因大模型计算开销高导致推理效率低等缺陷。为此，我们提出面向假新闻检测的LLM-引导多视角推理蒸馏框架（LLM-MRD）。学生模型从文本、视觉及跨模态三个视角构建综合基础；教师模型生成深层推理链作为丰富监督信号。核心校准蒸馏机制将复杂推理知识高效提炼至轻量学生模型。实验表明，LLM-MRD显著优于当前最优基线，在所有对比方法与数据集上平均准确率提升5.19%，假新闻检测F1值提升6.33%。代码已公开于https://github.com/Nasuro55/LLM-MRD。
原文摘要 · Abstract (English)
Multimodal fake news detection is crucial for mitigating societal disinformation. Existing approaches attempt to address this by fusing multimodal features or leveraging Large Language Models (LLMs) for advanced reasoning. However, these methods suffer from serious limitations, including a lack of comprehensive multi-view judgment and fusion, and prohibitive reasoning inefficiency due to the high computational costs of LLMs. To address these issues, we propose \textbf{LLM}-Guided \textbf{M}ulti-View \textbf{R}easoning \textbf{D}istillation for Fake News Detection ( \textbf{LLM-MRD}), a novel teacher-student framework. The Student Multi-view Reasoning module first constructs a comprehensive foundation from textual, visual, and cross-modal perspectives. Then, the Teacher Multi-view Reasoning module generates deep reasoning chains as rich supervision signals. Our core Calibration Distillation mechanism efficiently distills this complex reasoning-derived knowledge into the efficient student model. Experiments show LLM-MRD significantly outperforms state-of-the-art baselines. Notably, it demonstrates a comprehensive average improvement of 5.19\% in ACC and 6.33\% in F1-Fake when evaluated across all competing methods and datasets. Our code is available at https://github.com/Nasuro55/LLM-MRD
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。