Systematic Review of Hallucination Detection Methods for Large Language Models: A Complete Guide from Principles to Practice

Introduction / Main Floor: Systematic Review of Hallucination Detection Methods for Large Language Models: A Complete Guide from Principles to Practice

This article systematically organizes the core methods for hallucination detection in large language models, covering the classification system of factual hallucinations and faithfulness hallucinations, retrieval-augmented detection techniques, probabilistic measurement methods, and multi-model cross-validation strategies, providing practical references for building reliable AI applications.