用知识图谱生成难辨真伪的虚假信息，揭示大模型检测漏洞。
Leveraging Knowledge Graphs and LLMs for Structured Generation of Misinformation
- 基于知识图谱结构分析，自动挖掘似是而非的虚假三元组。
- 生成的虚假语句可信度高，人类难辨真伪，仅依赖公开数据。
- 发现当前大模型检测虚假信息能力不足，需改进检测策略。
虚假信息的快速传播，尤其在生成式AI技术推动下，对公众舆论、民主稳定和国家安全构成重大威胁。为深入理解和主动评估此类风险，需探索可系统化、可扩展的虚假信息生成方法。本文提出一种新方法：利用知识图谱（KG）作为结构化语义资源，系统生成虚假三元组。通过分析知识图谱中实体间距离及谓词关系等结构特性，识别出具有潜在欺骗性的虚假关系。这些三元组用于引导大语言模型（LLMs）生成不同程度可信度的虚假陈述。该确定性方法仅依赖公开可用的知识图谱（如WikiGraphs），生成的虚假信息对人类极具迷惑性。此外，我们研究了大语言模型在辨别真实与生成虚假信息方面的能力，结果表明现有基于大模型的检测方法存在显著局限，凸显了改进检测策略及深入探究生成模型内在偏见的必要性。
原文摘要 · Abstract (English)
The rapid spread of misinformation, further amplified by recent advances in generative AI, poses significant threats to society, impacting public opinion, democratic stability, and national security. Understanding and proactively assessing these threats requires exploring methodologies that enable structured and scalable misinformation generation. In this paper, we propose a novel approach that leverages knowledge graphs (KGs) as structured semantic resources to systematically generate fake triplets. By analyzing the structural properties of KGs, such as the distance between entities and their predicates, we identify plausibly false relationships. These triplets are then used to guide large language models (LLMs) in generating misinformation statements with varying degrees of credibility. By utilizing structured semantic relationships, our deterministic approach produces misinformation inherently challenging for humans to detect, drawing exclusively upon publicly available KGs (e.g., WikiGraphs). Additionally, we investigate the effectiveness of LLMs in distinguishing between genuine and artificially generated misinformation. Our analysis highlights significant limitations in current LLM-based detection methods, underscoring the necessity for enhanced detection strategies and a deeper exploration of inherent biases in generative models.
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