Developing a Scalable Benchmark for Assessing Large Language Models in Knowledge Graph Engineering

• URL: http://arxiv.org/abs/2308.16622v1
• Date: Thu, 31 Aug 2023 10:31:19 GMT
• Title: Developing a Scalable Benchmark for Assessing Large Language Models in Knowledge Graph Engineering
• Authors: Lars-Peter Meyer, Johannes Frey, Kurt Junghanns, Felix Brei, Kirill Bulert, Sabine Gr\"under-Fahrer, Michael Martin
• Abstract summary: We introduce a benchmarking framework focused on knowledge graph engineering (KGE) We show that while being a useful tool, Large Language Models are yet unfit to assist in knowledge graph generation with zero-shot prompting.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: As the field of Large Language Models (LLMs) evolves at an accelerated pace, the critical need to assess and monitor their performance emerges. We introduce a benchmarking framework focused on knowledge graph engineering (KGE) accompanied by three challenges addressing syntax and error correction, facts extraction and dataset generation. We show that while being a useful tool, LLMs are yet unfit to assist in knowledge graph generation with zero-shot prompting. Consequently, our LLM-KG-Bench framework provides automatic evaluation and storage of LLM responses as well as statistical data and visualization tools to support tracking of prompt engineering and model performance.

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