Hallucination Detection

Hallucination detection is the process of identifying claims in a model's output that are factually incorrect, unsupported by source documents, or internally contradictory. Detection methods range from automated NLI-based entailment checks and LLM judges to human expert review. In RAG systems, hallucination detection overlaps with faithfulness measurement, but it also targets errors introduced from parametric knowledge rather than retrieval gaps.