In the era of large language models (LLMs), hallucination (i.e., the tendency to generate factually incorrect content) poses great challenge to trustworthy and reliable deployment of LLMs in real-world applications. To tackle the LLM hallucination, three key questions should be well studied: how to detect hallucinations (detection), why do LLMs hallucinate (source), and what can be done to mitigate them (mitigation). To address these challenges, this work presents a systematic empirical study on LLM hallucination, focused on the the three aspects of hallucination detection, source and mitigation. Specially, we construct a new hallucination benchmark HaluEval 2.0, and designs a simple yet effective detection method for LLM hallucination. Furthermore, we zoom into the different training or utilization stages of LLMs and extensively analyze the potential factors that lead to the LLM hallucination. Finally, we implement and examine a series of widely used techniques to mitigate the hallucinations in LLMs. Our work has led to several important findings to understand the hallucination origin and mitigate the hallucinations in LLMs. Our code and data can be accessed at https://github.com/RUCAIBox/HaluEval-2.0.

翻译：在大型语言模型时代，幻觉（即生成事实上不正确内容的倾向）对LLM在现实应用中的可信赖和可靠部署构成了巨大挑战。为应对LLM幻觉，需深入研究三个关键问题：如何检测幻觉（检测）、为何产生幻觉（根源）以及如何缓解幻觉（缓解）。针对这些挑战，本工作围绕LLM幻觉的检测、根源与缓解三个方面开展了系统的实证研究。具体而言，我们构建了新的幻觉基准HaluEval 2.0，并提出了一种简单有效的LLM幻觉检测方法。进一步地，我们聚焦于LLM的不同训练或使用阶段，深入分析了可能导致LLM幻觉的潜在因素。最后，我们实施并检验了一系列用于缓解LLM幻觉的广泛使用技术。本工作揭示了关于理解幻觉起源与缓解LLM幻觉的多项重要发现。我们的代码和数据可访问https://github.com/RUCAIBox/HaluEval-2.0。