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Large Language Models (LLMs) are advancing at a rapid pace, with significantimprovements at natural language processing and coding tasks. Yet, theirability to work with formal languages representing data, specifically withinthe realm of knowledge graph engineering, remains under-investigated. Toevaluate the proficiency of various LLMs, we created a set of five tasks thatprobe their ability to parse, understand, analyze, and create knowledge graphsserialized in Turtle syntax. These tasks, each embodying distinct degrees ofcomplexity and being able to scale with the size of the problem, have beenintegrated into our automated evaluation system, the LLM-KG-Bench. Theevaluation encompassed four commercially available LLMs - GPT-3.5, GPT-4,Claude 1.3, and Claude 2.0, as well as two freely accessible offline models,GPT4All Vicuna and GPT4All Falcon 13B. This analysis offers an in-depthunderstanding of the strengths and shortcomings of LLMs in relation to theirapplication within RDF knowledge graph engineering workflows utilizing Turtlerepresentation. While our findings show that the latest commercial modelsoutperform their forerunners in terms of proficiency with the Turtle language,they also reveal an apparent weakness. These models fall short when it comes toadhering strictly to the output formatting constraints, a crucial requirementin this context.

Benchmarking the Abilities of Large Language Models for RDF Knowledge Graph Creation and Comprehension: How Well Do LLMs Speak Turtle?