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. 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. Our survey offers a structured guide for researchers aiming to fortify the factual reliability of LLMs. We consistently maintain and update the related open-source materials at https://github.com/wangcunxiang/LLM-Factuality-Survey.

中文翻译：

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大型语言模型中的事实性调查

本调查解决了大型语言模型 （LLM） 中事实性的关键问题。随着 LLM 在不同领域中找到应用，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 LLM 产生与既定事实不一致的内容的可能性。我们首先深入研究这些不准确的含义。随后，我们分析了 LLM 存储和处理事实的机制，寻找事实错误的主要原因。然后，我们的讨论过渡到评估 LLM 事实性的方法，强调关键指标、基准和研究。我们进一步探索提高 LLM 真实性的策略。我们的调查为旨在加强 LLM 事实可靠性的研究人员提供了结构化指南。我们始终如一地维护和更新 https://github.com/wangcunxiang/LLM-Factuality-Survey 的相关开源材料。