OAEI-LLM: A Benchmark Dataset for Understanding Large Language Model Hallucinations in Ontology Matching

Fuente: arXiv
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Autores principales: Qiang, Zhangcheng, Taylor, Kerry, Wang, Weiqing, Jiang, Jing
Formato: Preprint
Publicado: 2024
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author Qiang, Zhangcheng
Taylor, Kerry
Wang, Weiqing
Jiang, Jing
author_facet Qiang, Zhangcheng
Taylor, Kerry
Wang, Weiqing
Jiang, Jing
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2409_14038
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle OAEI-LLM: A Benchmark Dataset for Understanding Large Language Model Hallucinations in Ontology Matching
Qiang, Zhangcheng
Taylor, Kerry
Wang, Weiqing
Jiang, Jing
Artificial Intelligence
Computation and Language
Information Retrieval
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.
title OAEI-LLM: A Benchmark Dataset for Understanding Large Language Model Hallucinations in Ontology Matching
topic Artificial Intelligence
Computation and Language
Information Retrieval
url https://arxiv.org/abs/2409.14038