While large language models (LLMs) have demonstrated remarkable capabilities across a range of downstream tasks, a significant concern revolves around their propensity to exhibit hallucinations: LLMs occasionally generate content that diverges from the user input, contradicts previously generated context, or misaligns with established world knowledge. This phenomenon poses a substantial challenge to the reliability of LLMs in real-world scenarios. In this paper, we survey recent efforts on the detection, explanation, and mitigation of hallucination, with an emphasis on the unique challenges posed by LLMs. We present taxonomies of the LLM hallucination phenomena and evaluation benchmarks, analyze existing approaches aiming at mitigating LLM hallucination, and discuss potential directions for future research.

翻译：尽管大语言模型（LLMs）在一系列下游任务中展现出卓越能力，但其产生幻觉的倾向构成了重大隐忧：LLMs偶尔会生成偏离用户输入、与先前上下文矛盾或与既定世界知识不符的内容。这一现象对LLMs在现实场景中的可靠性构成重大挑战。本文系统梳理了近期在幻觉检测、解释与缓解方面的研究成果，重点探讨LLMs带来的独特挑战。我们提出了LLM幻觉现象与评估基准的分类体系，分析了现有旨在缓解LLM幻觉的方法，并展望了未来研究的潜在方向。