Testing hallucination detection systems

Creating effective hallucination detection requires systematic testing approaches beyond basic accuracy measures:

• Red teaming: Specialized teams deliberately try to provoke hallucinations to uncover edge cases.
• Adversarial testing: Testing with questions in areas of limited AI knowledge to trigger false information, such as questions about obscure topics.
• Benchmark testing: Measuring performance against curated sets of factual and counterfactual statements.

When evaluating these systems, track both false positive rates (legitimate content incorrectly flagged) and false negative rates (hallucinations missed). Different applications require different priorities. Healthcare might minimize false negatives at the cost of more false positives, while creative applications might accept more false negatives to maintain fluid experiences. Regular human evaluation remains essential, as AI-based hallucination detectors themselves can fail in unexpected ways.