  Data visualization is a powerful tool for exploring and communicating
insights in various domains. To automate visualization choice for datasets, a
task known as visualization recommendation has been proposed. Various
machine-learning-based approaches have been developed for this purpose, but
they often require a large corpus of dataset-visualization pairs for training
and lack natural explanations for their results. To address this research gap,
we propose LLM4Vis, a novel ChatGPT-based prompting approach to perform
visualization recommendation and return human-like explanations using very few
demonstration examples. Our approach involves feature description,
demonstration example selection, explanation generation, demonstration example
construction, and inference steps. To obtain demonstration examples with
high-quality explanations, we propose a new explanation generation
bootstrapping to iteratively refine generated explanations by considering the
previous generation and template-based hint. Evaluations on the VizML dataset
show that LLM4Vis outperforms or performs similarly to supervised learning
models like Random Forest, Decision Tree, and MLP in both few-shot and
zero-shot settings. The qualitative evaluation also shows the effectiveness of
explanations generated by LLM4Vis. We make our code publicly available at
\href{https://github.com/demoleiwang/LLM4Vis}{https://github.com/demoleiwang/LLM4Vis}.
