Text-to-Vis is an emerging task in the natural language processing (NLP) area that aims to automatically generate data visualizations from natural language questions (NLQs). Despite their progress, existing text-to-vis models often heavily rely on lexical matching between words in the questions and tokens in data schemas. This overreliance on lexical matching may lead to a diminished level of model robustness against input variations. In this study, we thoroughly examine the robustness of current text-to-vis models, an area that has not previously been explored. In particular, we construct the first robustness dataset nvBench-Rob, which contains diverse lexical and phrasal variations based on the original text-to-vis benchmark nvBench. Then, we found that the performance of existing text-to-vis models on this new dataset dramatically drops, implying that these methods exhibit inadequate robustness overall. Finally, we propose a novel framework based on Retrieval-Augmented Generation (RAG) technique, named GRED, specifically designed to address input perturbations in these two variants. The framework consists of three parts: NLQ-Retrieval Generator, Visualization Query-Retrieval Retuner and Annotation-based Debugger, which are used to tackle the challenges posed by natural language variants, programming style differences and data schema variants, respectively. Extensive experimental evaluations show that, compared to the state-of-the-art model RGVisNet in the Text-to-Vis field, GRED performs better in terms of model robustness, with a 32% increase in accuracy on the proposed nvBench-Rob dataset.

翻译：文本到可视化（Text-to-Vis）是自然语言处理领域的一项新兴任务，旨在根据自然语言问题（NLQs）自动生成数据可视化图表。尽管现有方法取得了进展，但它们通常过度依赖问题词汇与数据模式标记之间的词汇匹配。这种对词汇匹配的过度依赖可能导致模型对输入变体的鲁棒性降低。本研究首次系统考察了当前文本到可视化模型的鲁棒性，该方向此前尚未被探索。具体而言，我们基于原始文本到可视化基准数据集nvBench构建了首个鲁棒性数据集nvBench-Rob，其中包含多样化的词汇与短语变体。实验发现，现有文本到可视化模型在该新数据集上的性能显著下降，表明这些方法整体鲁棒性不足。最后，我们提出一种基于检索增强生成（RAG）技术的新型框架GRED，专门用于处理针对这两类变体的输入扰动。该框架包含三个组件：NLQ检索生成器、可视化查询检索调优器和基于注释的调试器，分别应对自然语言变体、编程风格差异和数据模式变体带来的挑战。大量实验评估表明，与文本到可视化领域的最优模型RGVisNet相比，GRED在模型鲁棒性方面表现更优，在提出的nvBench-Rob数据集上准确率提升了32%。