Surface Reading LLMs: Synthetic Text and its Styles
This work addresses the societal impact of LLMs on communication and meaning for researchers in AI and semiotics, though it is incremental in integrating stylistic analysis with existing critiques.
The paper tackles the problem of understanding how large language models (LLMs) reshape meaning-making by focusing on their stylistic generation of synthetic text, arguing that analyzing surface-level styles reveals LLMs as cultural machines that transform discourse conditions.
Despite a potential plateau in ML advancement, the societal impact of large language models lies not in approaching superintelligence but in generating text surfaces indistinguishable from human writing. While Critical AI Studies provides essential material and socio-technical critique, it risks overlooking how LLMs phenomenologically reshape meaning-making. This paper proposes a semiotics of "surface integrity" as attending to the immediate plane where LLMs inscribe themselves into human communication. I distinguish three knowledge interests in ML research (epistemology, epistēmē, and epistemics) and argue for integrating surface-level stylistic analysis alongside depth-oriented critique. Through two case studies examining stylistic markers of synthetic text, I argue how attending to style as a semiotic phenomenon reveals LLMs as cultural machines that transform the conditions of meaning emergence and circulation in contemporary discourse, independent of questions about machine consciousness.