Developing a Scalable Benchmark for Assessing Large Language Models in Knowledge Graph Engineering
This provides a scalable benchmark for researchers and practitioners to evaluate LLMs in knowledge graph engineering, though it is incremental as it builds on existing benchmarking efforts.
The paper tackled the problem of assessing Large Language Models (LLMs) in knowledge graph engineering by introducing a benchmarking framework called LLM-KG-Bench, which includes challenges for syntax correction, facts extraction, and dataset generation, and found that LLMs are currently unfit for zero-shot knowledge graph generation.
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.