Summary of Oaei-llm: a Benchmark Dataset For Understanding Large Language Model Hallucinations in Ontology Matching, by Zhangcheng Qiang et al.
OAEI-LLM: A Benchmark Dataset for Understanding Large Language Model Hallucinations in Ontology Matching
by Zhangcheng Qiang, Kerry Taylor, Weiqing Wang, Jing Jiang
First submitted to arxiv on: 21 Sep 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); Information Retrieval (cs.IR)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel approach to benchmarking ontology matching (OM) for large language models (LLMs) is presented, addressing the issue of LLM hallucinations commonly observed in domain-specific tasks. The proposed OAEI-LLM dataset extends existing Ontology Alignment Evaluation Initiative (OAEI) datasets to evaluate LLM-specific hallucinations in OM tasks. This work outlines the methodology used in dataset construction and schema extension, highlighting potential use cases. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models are often used for ontology matching, but they can be fooled into creating false information. To understand this better, researchers created a new dataset called OAEI-LLM. This dataset is based on existing datasets that test how well large language models perform in matching ontologies. The new dataset helps us see how these models do when they’re trying to match ontologies but might not be accurate. |
Keywords
» Artificial intelligence » Alignment
