Authoring data-driven articles is a complex process requiring authors to not only analyze data for insights but also craft a cohesive narrative that effectively communicates the insights. Text generation capabilities of contemporary large language models (LLMs) present an opportunity to assist the authoring of data-driven articles and expedite the writing process. In this work, we investigate the feasibility and perceived value of leveraging LLMs to support authors of data-driven articles. We designed a prototype system, DataTales, that leverages a LLM to generate textual narratives accompanying a given chart. Using DataTales as a design probe, we conducted a qualitative study with 11 professionals to evaluate the concept, from which we distilled affordances and opportunities to further integrate LLMs as valuable data-driven article authoring assistants.

翻译：创作数据驱动文章是一个复杂的过程，作者不仅需要分析数据以获取洞见，还需构建连贯的叙事来有效传达这些洞见。当代大型语言模型的文本生成能力为辅助数据驱动文章的创作、加速写作流程提供了契机。本研究探讨了利用大型语言模型支持数据驱动文章作者的可行性与感知价值。我们设计了一个原型系统DataTales，该系统借助大型语言模型为给定图表生成对应的文本叙事。以DataTales为设计探针，我们与11位专业人士开展了定性研究以评估该概念，从中提炼出进一步将大型语言模型整合为有价值的数据驱动文章创作助手的效能与机遇。