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}{this https URL}.

LLM4Vis 是一个基于 ChatGPT 的提示方法，可执行可视化推荐，并使用极少数量的演示示例返回类人的解释，通过考虑先前生成的解释和基于模板的提示进行迭代性微调生成的解释。在 VizML 数据集上的评估表明，LLM4Vis 在少量示例和零示例设置中优于或类似于 Random Forest、Decision Tree 和 MLP 等监督学习模型，并通过定性评估显示 LLM4Vis 生成的解释的有效性。