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

• URL: http://arxiv.org/abs/2308.04076v1
• Date: Tue, 8 Aug 2023 06:21:58 GMT
• Title: DataTales: Investigating the use of Large Language Models for Authoring Data-Driven Articles
• Authors: Nicole Sultanum, Arjun Srinivasan
• Abstract summary: Large language models (LLMs) present an opportunity to assist the authoring 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.
• Score: 19.341156634212364
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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