Natural-language-to-visualization (NL2VIS) systems based on large language models (LLMs) have substantially improved the accessibility of data visualization. However, their further adoption is hindered by two coupled challenges: (i) the absence of standardized evaluation metrics makes it difficult to assess progress in the field and compare different approaches; and (ii) natural language descriptions are inherently underspecified, so multiple visualizations may be valid for the same query. To address these issues, we introduce VegaChat, a framework for generating, validating, and assessing declarative visualizations from natural language. We propose two complementary metrics: Spec Score, a deterministic metric that measures specification-level similarity without invoking an LLM, and Vision Score, a library-agnostic, image-based metric that leverages a multimodal LLM to assess chart similarity and prompt compliance. We evaluate VegaChat on the NLV Corpus and on the annotated subset of ChartLLM. VegaChat achieves near-zero rates of invalid or empty visualizations, while Spec Score and Vision Score exhibit strong correlation with human judgments (Pearson 0.65 and 0.71, respectively), indicating that the proposed metrics support consistent, cross-library comparison. The code and evaluation artifacts are available at https://zenodo.org/records/17062309.

翻译：基于大语言模型（LLM）的自然语言到可视化（NL2VIS）系统显著提升了数据可视化的可访问性。然而，其进一步应用受到两个相互关联的挑战的阻碍：（i）缺乏标准化的评估指标，使得难以衡量该领域的进展并比较不同方法；（ii）自然语言描述本身存在欠确定性，同一查询可能对应多个有效的可视化方案。为解决这些问题，我们提出了VegaChat，一个从自然语言生成、验证和评估声明式可视化的框架。我们提出了两个互补的指标：Spec Score（一种无需调用LLM、在规范层面衡量相似性的确定性指标）和Vision Score（一种与可视化库无关、基于图像的指标，其利用多模态大语言模型评估图表相似性与提示符合度）。我们在NLV语料库和ChartLLM的标注子集上对VegaChat进行了评估。VegaChat实现了接近零的无效或空可视化生成率，同时Spec Score和Vision Score与人工判断表现出强相关性（皮尔逊相关系数分别为0.65和0.71），表明所提指标支持跨可视化库的一致性比较。代码与评估材料已发布于 https://zenodo.org/records/17062309。