Large Language Models Threaten Language's Epistemic and Communicative Foundations

Large language models are reshaping the norms of human communication, sometimes decoupling words from genuine human thought. This transformation is deep, and undermines norms historically tied to authorship of text. We draw from linguistic philosophy and AI ethics to detail how large-scale text generation can induce semantic drift, erode accountability, and obfuscate intent and authorship. Our work here introduces hybrid authorship graphs (modeling humans, LLMs, and texts in a provenance network), epistemic doppelgängers (LLM-generated texts that are indistinguishable from human-authored texts), and authorship entropy. We explore mechanisms such as "proof-of-interaction" authorship verification and educational reforms to restore confidence in language. LLMs' benefits (broader access, increased fluency, automation, etc.) are undeniable, but the upheavals they introduce to the linguistic landscape demand reckoning.