This research introduces LapEigvals, a new method that leverages graph theory to detect hallucinations in Large Language Models by analyzing the spectral features of attention maps.

• Interprets attention maps as graph structures to identify mathematical signatures of hallucinations
• Provides a more reliable detection mechanism for false information generated by LLMs
• Addresses a critical security challenge in AI deployment for safety-critical applications
• Offers a technical approach that could help organizations implement stronger safeguards against AI misinformation

This methodology is particularly valuable for security applications where LLM reliability is non-negotiable, such as healthcare, finance, and critical infrastructure, helping organizations reduce risk when deploying generative AI technologies.

Hallucination Detection in LLMs Using Spectral Features of Attention Maps