Large language models (LLMs) have been widely employed for graph-to-text generation tasks.However, the process of finetuning LLMs requires significant training resources and annotation work.In this paper, we explore the capability of generative models to generate descriptive text from graph data in a zeroshot setting.Specifically, we evaluate GPT-3 and ChatGPT on two graph-to-text datasets and compare their performance with that of finetuned LLM models such as T5 and BART.Our results demonstrate that generative mo…