This research introduces a novel approach to multi-hop fact verification by integrating structured knowledge into large language models, significantly improving accuracy in complex claim verification.

• Addresses the challenge of verifying claims requiring multiple pieces of evidence with complex logical relationships
• Implements a knowledge-augmented framework that enhances both understanding and reasoning abilities of LLMs
• Demonstrates superior performance in detecting sophisticated misinformation compared to traditional semantic-based approaches
• Offers a security-focused solution to combat the rapid spread of false information on social platforms

This advancement is particularly valuable for security applications where detecting increasingly sophisticated misinformation is critical for maintaining information integrity and public trust.

Enhancing Multi-Hop Fact Verification with Structured Knowledge-Augmented Large Language Models