This research addresses a critical security challenge in AI by developing more effective methods to detect hallucinations in Large Language Models (LLMs).

• Current self-consistency techniques for hallucination detection are approaching their performance ceiling
• The paper introduces cross-model verification as a more reliable approach
• Demonstrates significant improvement in hallucination detection through model collaboration
• Particularly valuable for sensitive applications where reliability is critical

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