On the Question of Authorship in Large Language Models (LLMs)

Abstract

The adoption of pre-trained large language models (LLMs), like ChatGPT, across an increasingly diverse range of tasks
and domains poses significant challenges for authorial attribution and other basic knowledge organization practices. This
paper examines the theoretical and practical issues introduced by LLMs and describes how their use erodes the supposedly
firm boundaries separating specific works and creators. Building upon the author-as-node framework proposed by Soos
and Leazer (2020), we compare works created with and without the use of LLMs; ultimately, we argue that the issues
associated with these novel tools are indicative of preexisting limitations within standard entity-relationship models. As the
growing popularity of generative AI raises concerns about plagiarism, academic integrity, and intellectual property, we
encourage a reevaluation of reductive work/creator associations and advocate for the adoption of a more expansive
approach to authorship.