Large language models often produce hallucinated content involving factual errors, input contradictions, or internal inconsistencies. Existing detection methods typically address only one type, leading to incomplete coverage. This paper presents a hierarchical detection framework that sequentially evaluates fact, input, and context conflicts using early-stopping logic. Once a hallucination is identified, processing halts with a confidence-graded classification reflecting severity. The framework achieves computational efficiency, comprehensive coverage, and interpretable confidence levels that prioritize factual accuracy over subjective consistency.
Hu et al. (Mon,) studied this question.