LLM Hallucination Detection: Methods and Limits

LLM hallucination detection assesses whether model outputs are supported by available evidence, since fluent language alone does not indicate correctness, and aims to contain rather than eliminate errors inherent in probabilistic generation. Key approaches include inexpensive deterministic checks for formatting and citations, groundedness scoring that evaluates whether individual claims are entailed by retrieved sources, semantic entropy that identifies meaning-level disagreement across repeated generations, LLM-as-a-judge methods using a second model and rubric, and fine-tuned classifiers for high-volume use. Each method has structural limitations: groundedness cannot validate claims without retrieval or faulty sources, semantic entropy misses consistent but incorrect answers, judges can inherit model biases, and trained detectors may not recognize new failure patterns. Effective deployment therefore layers complementary methods, applies stricter and more costly checks to high-risk use cases, validates retrieval quality separately, and regularly tests detectors against labelled examples of unsupported outputs.

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