We introduce VL2NL, a Large Language Model (LLM) framework that generates rich and diverse NL datasets using only Vega-Lite specifications as input, thereby streamlining the development of Natural Language Interfaces (NLIs) for data visualization. To synthesize relevant chart semantics accurately and enhance syntactic diversity in each NL dataset, we leverage 1) a guided discovery incorporated into prompting so that LLMs can steer themselves to create faithful NL datasets in a self-directed manner; 2) a score-based paraphrasing to augment NL syntax along with four language axes. We also present a new collection of 1,981 real-world Vega-Lite specifications that have increased diversity and complexity than existing chart collections. When tested on our chart collection, VL2NL extracted chart semantics and generated L1/L2 captions with 89.4% and 76.0% accuracy, respectively. It also demonstrated generating and paraphrasing utterances and questions with greater diversity compared to the benchmarks. Last, we discuss how our NL datasets and framework can be utilized in real-world scenarios. The codes and chart collection are available at https://github.com/hyungkwonko/chart-llm.

翻译：我们提出VL2NL框架——一种利用大语言模型（LLM）仅以Vega-Lite规范文件为输入即可生成丰富多样自然语言（NL）数据集的系统，从而简化数据可视化自然语言界面（NLI）的开发流程。为精准合成相关图表语义并增强每个NL数据集的句法多样性，我们采用两种策略：1）将引导式发现机制融入提示词设计，使大语言模型能以自主导向方式生成忠实于原图的NL数据集；2）基于评分的释义方法，沿四个语言维度增强NL句法多样性。我们同时发布了包含1,981个真实世界Vega-Lite规范文件的图表数据集，该集合在多样性和复杂度上均超越现有图表库。在图表数据集上的测试表明，VL2NL框架分别以89.4%和76.0%的准确率提取图表语义并生成L1/L2级别图表说明。与基准方法相比，该框架生成和改写的表述与问题具有更高多样性。最后，我们探讨了所提出的NL数据集与框架在现实场景中的应用潜力。相关代码与图表数据集已公开于https://github.com/hyungkwonko/chart-llm。