On the Question of Authorship in Large Language Models (LLMs)
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, and encourages a reevaluation of reductive work/creator associations and advocate for the adoption of a more expansive approach to authorship.
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.