This research introduces a novel cross-model verification technique that outperforms existing self-consistency methods for hallucination detection in LLMs.

• Self-consistency methods alone have nearly reached their performance ceiling
• Cross-model verification significantly improves hallucination detection in black-box settings
• Particularly effective for sensitive security applications where reliability is crucial
• Provides a practical framework for verification when model uncertainty is detected

The approach addresses a critical security challenge by enabling more reliable verification in situations where LLMs express uncertainty, reducing risks in high-stakes applications like security systems and enterprise deployments.

Verify when Uncertain: Beyond Self-Consistency in Black Box Hallucination Detection