🤖 AI Summary
This paper systematically surveys hallucination in large language models (LLMs), clarifying its root causes—including training data bias, decoding-induced distortion, and incomplete knowledge representation—and classifying its multidimensional manifestations (e.g., factual, logical, and consistency hallucinations). Methodologically, it proposes a unified hallucination taxonomy and establishes a three-layer mitigation framework—detection, attribution, and suppression—that integrates major technical paradigms: pretraining interventions, prompt engineering, retrieval-augmented generation, and post-hoc correction. Through structured literature analysis and mechanistic dissection of representative approaches, the work delivers the first practice-oriented, one-stop knowledge portal for hallucination governance. The resulting concise survey document enhances research reproducibility and accelerates real-world deployment, providing both theoretical foundations and methodological guidance for advancing LLM trustworthiness.
📝 Abstract
Traditional language models face a challenge from hallucinations. Their very presence casts a large, dangerous shadow over the promising realm of natural language processing. It becomes crucial to understand the various kinds of hallucinations that occur nowadays, their origins, and ways of reducing them. This document provides a concise and straightforward summary of that. It serves as a one-stop resource for a general understanding of hallucinations and how to mitigate them.