Predicting Hallucinations before LLMs Make Them: Combining External Knowledge with Behavioral Uncertainty for Black-Box Auditing of Large Language Models

Large language models often produce confident but factually incorrect answers, a phenomenonknown as hallucination, particularly on questions involving long-tail entities that appear infrequently in training data. Predicting which questions will trigger hallucinations is critical for safe deployment,