Hallucinations of large language models (LLMs) commonly occur in domain-specific downstream tasks, with no exception in ontology matching (OM). The prevalence of using LLMs for OM raises the need for benchmarks to better understand LLM hallucinations. The OAEI-LLM dataset is an extended version of the Ontology Alignment Evaluation Initiative (OAEI) datasets that evaluate LLM-specific hallucinations in OM tasks. We outline the methodology used in dataset construction and schema extension, and provide examples of potential use cases.

翻译：大语言模型（LLMs）的幻觉现象在特定领域下游任务中普遍存在，实体对齐（OM）任务亦不例外。随着LLMs在实体对齐中的广泛应用，亟需建立基准数据集以深入理解其幻觉行为。OAEI-LLM数据集是本体对齐评估倡议（OAEI）数据集的扩展版本，专门用于评估LLMs在实体对齐任务中特有的幻觉现象。本文阐述了数据集构建与模式扩展的方法论，并提供了潜在应用场景的示例。