  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.

Human-Computer Interaction;Computation and Language