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Authoring data-driven articles is a complex process requiring authors to notonly analyze data for insights but also craft a cohesive narrative thateffectively communicates the insights. Text generation capabilities ofcontemporary large language models (LLMs) present an opportunity to assist theauthoring of data-driven articles and expedite the writing process. In thiswork, we investigate the feasibility and perceived value of leveraging LLMs tosupport authors of data-driven articles. We designed a prototype system,DataTales, that leverages a LLM to generate textual narratives accompanying agiven chart. Using DataTales as a design probe, we conducted a qualitativestudy with 11 professionals to evaluate the concept, from which we distilledaffordances and opportunities to further integrate LLMs as valuable data-drivenarticle authoring assistants.

DataTales: Investigating the use of Large Language Models for Authoring Data-Driven Articles