Leveraging Knowledge Graphs and LLMs for Structured Generation of Misinformation
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.
Code (0)
등록된 구현이 없습니다.
Tasks
Knowledge GraphsMisinformationSimilar Papers 제목 키워드 기반
Schema Generation for Large Knowledge Graphs Using Large Language Models
Schemas are vital for ensuring data quality in the Semantic Web and natural language processing. Traditionally, their creation demands substantial involvement from knowledge engineers and domain experts. Leveraging the i…
Knowledge GraphsThinking with Knowledge Graphs: Enhancing LLM Reasoning Through Structured Data
Large Language Models (LLMs) have demonstrated remarkable capabilities in natural language understanding and generation. However, they often struggle with complex reasoning tasks and are prone to hallucination. Recent re…
HallucinationKnowledge GraphsNatural Language UnderstandingAI Agent-Driven Framework for Automated Product Knowledge Graph Construction in E-Commerce
The rapid expansion of e-commerce platforms generates vast amounts of unstructured product data, creating significant challenges for information retrieval, recommendation systems, and data analytics. Knowledge Graphs (KG…
Recommendation SystemsInformation RetrievalKnowledge GraphsKG-CQR: Leveraging Structured Relation Representations in Knowledge Graphs for Contextual Query Retrieval
The integration of knowledge graphs (KGs) with large language models (LLMs) offers significant potential to improve the retrieval phase of retrieval-augmented generation (RAG) systems. In this study, we propose KG-CQR, a…
Multi-hop Question AnsweringKnowledge GraphsmKG-RAG: Leveraging Multimodal Knowledge Graphs in Retrieval-Augmented Generation for Knowledge-intensive VQA
Retrieval-Augmented Generation (RAG) has emerged as an effective paradigm for expanding the knowledge capacity of Multimodal Large Language Models (MLLMs) by incorporating external knowledge sources into the generation p…
Visual Question AnsweringKnowledge Graphs