The emergence of large language models (LLMs) has marked a significant breakthrough in natural language processing (NLP), leading to remarkable advancements in text understanding and generation. Nevertheless, alongside these strides, LLMs exhibit a critical tendency to produce hallucinations, resulting in content that is inconsistent with real-world facts or user inputs. This phenomenon poses substantial challenges to their practical deployment and raises concerns over the reliability of LLMs in real-world scenarios, which attracts increasing attention to detect and mitigate these hallucinations. In this survey, we aim to provide a thorough and in-depth overview of recent advances in the field of LLM hallucinations. We begin with an innovative taxonomy of LLM hallucinations, then delve into the factors contributing to hallucinations. Subsequently, we present a comprehensive overview of hallucination detection methods and benchmarks. Additionally, representative approaches designed to mitigate hallucinations are introduced accordingly. Finally, we analyze the challenges that highlight the current limitations and formulate open questions, aiming to delineate pathways for future research on hallucinations in LLMs.

翻译：大型语言模型（LLM）的出现标志着自然语言处理（NLP）领域的重大突破，在文本理解与生成方面取得了显著进展。然而，与此同时，LLM表现出产生幻觉的关键倾向，导致生成内容与真实世界事实或用户输入不一致。这一现象对其实际部署构成重大挑战，并引发了对LLM在真实场景中可靠性的担忧，促使学界日益关注如何检测与缓解这些幻觉。本综述旨在系统全面地概述LLM幻觉领域的最新研究进展。我们首先提出LLM幻觉的创新性分类体系，进而深入剖析导致幻觉的成因因素。随后，全面综述幻觉检测方法与评估基准，并相应介绍代表性缓解技术。最后，本文分析当前局限性所凸显的挑战，并提出开放性问题，旨在勾勒未来LLM幻觉研究的发展路径。