Towards an Authorial Leverage Evaluation Framework for Expressive Benefits of Deep Generative Models in Story Writing

What are dimensions of human intent, and how do writing tools shape and augment these expressions? From papyrus to auto-complete, a major turning point was when Alan Turing famously asked, “Can Machines Think?” If so, should we offload aspects of our thinking to machines, and what impact do they have in enabling the intentions we have? This paper adapts the Authorial Leverage framework, from the Intelligent Narrative Technologies literature, for evaluating recent generative model advancements. With increased widespread access to Large Language Models (LLMs), the evolution of our evaluative frameworks follow suit. To do this, we discuss previous expert studies of deep generative models for fiction writers and playwrights, and propose two future directions, (1) author-focused and (2) audience-focused, for furthering our understanding of Authorial Leverage of LLMs, particularly in the domain of comedy writing.