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，一种仅以Vega-Lite规范为输入即可生成丰富多样自然语言数据集的大语言模型框架，从而简化数据可视化自然语言界面的开发。为准确合成相关图表语义并增强每个自然语言数据集的句法多样性，我们利用：1) 融入提示的引导式发现机制，使大语言模型能够以自主方式生成忠实于原意的自然语言数据集；2) 基于分数的释义生成方法，沿四个语言维度增强自然语言句法。我们还提出包含1,981个真实世界Vega-Lite规范的新数据集，其多样性和复杂性均优于现有图表数据集。在我们的图表数据集测试中，VL2NL提取图表语义并生成L1/L2描述的准确率分别达到89.4%和76.0%。与基准方法相比，该框架在生成和释义话语及问题时展现出更高的多样性。最后，我们讨论了自然语言数据集及框架在实际场景中的应用潜力。代码和图表数据集已开源：https://github.com/hyungkwonko/chart-llm。