This survey addresses the crucial issue of factuality in Large Language Models (LLMs). As LLMs find applications across diverse domains, the reliability and accuracy of their outputs become vital. We define the Factuality Issue as the probability of LLMs to produce content inconsistent with established facts. We first delve into the implications of these inaccuracies, highlighting the potential consequences and challenges posed by factual errors in LLM outputs. Subsequently, we analyze the mechanisms through which LLMs store and process facts, seeking the primary causes of factual errors. Our discussion then transitions to methodologies for evaluating LLM factuality, emphasizing key metrics, benchmarks, and studies. We further explore strategies for enhancing LLM factuality, including approaches tailored for specific domains. We focus two primary LLM configurations standalone LLMs and Retrieval-Augmented LLMs that utilizes external data, we detail their unique challenges and potential enhancements. Our survey offers a structured guide for researchers aiming to fortify the factual reliability of LLMs.

翻译：本综述探讨了大语言模型（LLMs）中事实性这一关键问题。随着LLMs在多个领域的应用扩展，其输出的可靠性和准确性变得至关重要。我们将事实性问题定义为LLMs生成与既定事实不一致内容的概率。首先深入分析这些不准确性的影响，强调LLM输出中事实错误带来的潜在后果与挑战。随后剖析LLMs存储和处理事实的机制，探究事实错误的主要成因。继而转向评估LLM事实性的方法论，重点讨论关键指标、基准与相关研究。进一步探索增强LLM事实性的策略，包括针对特定领域定制的方法。我们聚焦两种主要LLM配置——独立LLM和利用外部数据的检索增强型LLM，详细阐述它们的独特挑战与潜在改进方案。本综述旨在为致力于提升LLM事实可靠性的研究者提供结构化指导。