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.

本研究探讨使用大型语言模型（LLMs）支持数据驱动文章创作的可行性和感知价值，设计了一个原型系统 DataTales，通过使用 LLMs 生成与给定图表相关的文本叙述，并通过与 11 名专业人士进行定性研究评估了该概念，从中提炼了 LLMs 作为有价值的数据驱动文章创作助手的优点和机会。