{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/developing-a-scalable-benchmark-for-assessing","title":"Developing a Scalable Benchmark for Assessing Large Language Models in Knowledge Graph Engineering","arxiv_id":"2308.16622","date":"2023-08-31","proceeding":null,"authors":["Lars-Peter Meyer","Johannes Frey","Kurt Junghanns","Felix Brei","Kirill Bulert","Sabine Gründer-Fahrer","Michael Martin"],"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.","url_abs":"https://arxiv.org/abs/2308.16622v1","url_pdf":"https://arxiv.org/pdf/2308.16622v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"developing-a-scalable-benchmark-for-assessing","repo_url":"https://github.com/aksw/llm-kg-bench","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null},{"paper_slug":"developing-a-scalable-benchmark-for-assessing","repo_url":"https://github.com/aksw/llm-kg-bench-results","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"benchmarking","task_name":"Benchmarking"},{"task_slug":"dataset-generation","task_name":"Dataset Generation"},{"task_slug":"graph-generation","task_name":"Graph Generation"},{"task_slug":"knowledge-graphs","task_name":"Knowledge Graphs"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2308.16622","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}