How do you evaluate hallucination rates in LLM systems?

Hallucination evaluation measures how often LLM outputs contain unsupported or incorrect information.

Hallucination evaluation is difficult because correctness can be subjective and context-dependent.

Common evaluation methods include:

Experts manually verify factual correctness.

Checking whether generated claims are supported by retrieved documents.

Using verifier models or external knowledge bases.

Using secondary LLMs to assess factual consistency.

Metrics may include hallucination rate, unsupported claim frequency, contradiction score, or factual precision.…