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。

LLM4Vis：使用 ChatGPT 进行解释性可视化推荐

LLM4Vis: Explainable Visualization Recommendation using ChatGPT

Selecting the appropriate visual presentation of the data such that it
preserves the semantics of the underlying data and at the same time provides an
intuitive summary of the data is an important, often the final step of data
analytics. Unfortunately, this is also a step involving significant human
effort starting from selection of groups of columns in the structured results
from analytics stages, to the selection of right visualization by experimenting
with various alternatives. In this paper, we describe our \emph{DataVizard}
system aimed at reducing this overhead by automatically recommending the most
appropriate visual presentation for the structured result. Specifically, we
consider the following two scenarios: first, when one needs to visualize the
results of a structured query such as SQL; and the second, when one has
acquired a data table with an associated short description (e.g., tables from
the Web). Using a corpus of real-world database queries (and their results) and
a number of statistical tables crawled from the Web, we show that DataVizard is
capable of recommending visual presentations with high accuracy. We also
present the results of a user survey that we conducted in order to assess user
views of the suitability of the presented charts vis-a-vis the plain text
captions of the data.

该研究介绍了一种名为 DataVizard 的系统，该系统可以自动推荐最佳的数据可视化方案，以提高结构化查询结果的可视化效率，并通过用户调查验证其准确性。