Not true but believable

Abstract A defining if under-acknowledged feature of large language models (LLMs) is their non-indexicality: what AI engines “know” about the world reflects only their ability to predict and successfully imitate existing textual representations of that world. Like fictional narratives, therefore, they traffic not in truth or falsity, but believability. Helpfully, literary critics have experience thinking critically about the ways in which believability is shaped by existing ideologies and biases — an acknowledged limitation of AI-generated texts as well. Drawing on Neilsen, Phelan and Walsh’s rhetorical model of fictionality, this article uses Chimamanda Ngozi Adichie’s metafictional short story, “Jumping Monkey Hill” as a laboratory to explore the relationship between fictionality and believability in a situation where rhetorical cues about a text’s ontological status are misleading. From there, I suggest how literary ways of reading might inform our engagement with AI texts — including and especially those that present themselves as factual.