Recent progress in generative AI, including large language models (LLMs) like ChatGPT, has opened up significant opportunities in fields ranging from natural language processing to knowledge discovery and data mining. However, there is also a growing awareness that the models can be prone to problems such as making information up or `hallucinations', and faulty reasoning on seemingly simple problems. Because of the popularity of models like ChatGPT, both academic scholars and citizen scientists have documented hallucinations of several different types and severity. Despite this body of work, a formal model for describing and representing these hallucinations (with relevant meta-data) at a fine-grained level, is still lacking. In this paper, we address this gap by presenting the Hallucination Ontology or HALO, a formal, extensible ontology written in OWL that currently offers support for six different types of hallucinations known to arise in LLMs, along with support for provenance and experimental metadata. We also collect and publish a dataset containing hallucinations that we inductively gathered across multiple independent Web sources, and show that HALO can be successfully used to model this dataset and answer competency questions.

翻译：近年来，生成式人工智能的进展，包括ChatGPT等大语言模型，为自然语言处理、知识发现与数据挖掘等领域带来了重要机遇。然而，人们也逐渐认识到，这些模型容易产生信息编造（即"幻觉"）以及看似简单问题上的错误推理等问题。由于ChatGPT等模型的普及，学术界学者和公民科学家已记录下多种类型和严重程度的幻觉。尽管已有大量相关工作，但目前仍缺乏一种能够在细粒度层面描述和表示这些幻觉（附带相关元数据）的正式模型。本文通过提出"HALO"（幻觉本体）来填补这一空白。这是一个用OWL编写的正式、可扩展的本体，目前支持大语言模型中已知的六类幻觉，同时提供来源追踪和实验元数据支持。我们还收集并发布了一个数据集，其中包含从多个独立网络来源归纳整理出的幻觉实例，并证明HALO能够有效用于建模该数据集并回答能力问题。