Hallucination Detection and Mitigation in Large Language Models: A Comprehensive Review

Abstract

Large Language Models have achieved unprecedented capabilities in natural language generation but remain vulnerable to hallucinations—outputs that are fluent and plausible yet factually incorrect or ungrounded. This comprehensive review examines the current landscape of hallucination detection and mitigation in LLMs, analyzing the theoretical foundations, detection methodologies, and mitigation strategies that have emerged to address this critical challenge. To explore the fundamental taxonomy distinguishing intrinsic hallucinations that deviate from input content from extrinsic hallucinations that contradict real-world facts, while examining how these manifestations vary across different natural language generation tasks. The review synthesizes five primary detection approaches including uncertainty estimation, attention pattern analysis, self-consistency checks, external fact verification, and trained evaluators, each offering unique advantages for identifying hallucinated content. To analyze mitigation strategies at both architectural levels through techniques like retrieval-augmented generation, tool integration, and factual fine-tuning, and systemic levels through guardrails, fallback policies, and human oversight. The evaluation landscape is examined through diverse benchmarks ranging from general-purpose frameworks to domain-specific assessments, with particular emphasis on the growing importance of multilingual and multimodal evaluation. The analysis reveals that while complete elimination of hallucinations is theoretically impossible in sufficiently complex models, a layered combination of improved architectures, rigorous detection methods, and systemic defenses offers the most effective path toward safe and trustworthy LLM deployment across critical applications.