Prompt4Vis: Prompting Large Language Models with Example Mining and
  Schema Filtering for Tabular Data Visualization
Shuaimin Li Xuanang Chen Yuanfeng Song Yunze Song Chen Zhang
Data visualization (DV) systems are increasingly recognized for their
profound capability to uncover insights from vast datasets, gaining attention
across both industry and academia. Crafting data queries is an essential
process within certain declarative visualization languages (DVLs, e.g.,
Vega-Lite, EChart.). The evolution of natural language processing (NLP)
technologies has streamlined the use of natural language interfaces to
visualize tabular data, offering a more accessible and intuitive user
experience. However, current methods for converting natural language questions
into data visualization queries, such as Seq2Vis, ncNet, and RGVisNet, despite
utilizing complex neural network architectures, still fall short of
expectations and have great room for improvement.
  Large language models (LLMs) such as ChatGPT and GPT-4, have established new
benchmarks in a variety of NLP tasks, fundamentally altering the landscape of
the field. Inspired by these advancements, we introduce a novel framework,
Prompt4Vis, leveraging LLMs and in-context learning to enhance the performance
of generating data visualization from natural language. Prompt4Vis comprises
two key components: (1) a multi-objective example mining module, designed to
find out the truly effective examples that strengthen the LLM's in-context
learning capabilities for text-to-vis; (2) a schema filtering module, which is
proposed to simplify the schema of the database. Extensive experiments through
5-fold cross-validation on the NVBench dataset demonstrate the superiority of
Prompt4Vis, which notably surpasses the state-of-the-art (SOTA) RGVisNet by
approximately 35.9% and 71.3% on dev and test sets, respectively. To the best
of our knowledge, Prompt4Vis is the first work that introduces in-context
learning into the text-to-vis for generating data visualization queries.