Treffer: Large language models and the problem of rhetorical debt.

This article offers broadly useful guidance for society's adaptation to the omnipresence of generative AI, with implications for every profession and academic discipline that involves writing or coding (recognized by some as a form of writing). Offering an interdisciplinary perspective grounded in the digital humanities, software development and writing across the curriculum, and building on performance historian Christopher Grobe's research on the role of arts and humanities expertise in AI development, I offer redefinitions of training data and prompt engineering. These essential yet misleading terms obscure the critical roles that humanities-based expertise has played in the development of GPTs and must play in guiding society's adaptation to generative AI. I also briefly review scholarship on what constitutes "writing" and what it means to teach writing. Next, I reflect on long-terms trends, in professional software development, of code sharing and reliance on automation, and the likely impact of imposing similar practices in professional writing. After identifying the fundamental problem of rhetorical debt and outlining its consequences, I further motivate my argument, in relation to the new economic value of expert writing. This new economic value necessitates a revaluation of the humanities—not only by computer science, the tech industry, and schools and universities, but by humanists themselves. [ABSTRACT FROM AUTHOR]

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