Beyond "AI Language": The case for the idiolectal nature of LLM output

A new arXiv paper (2608.06589v1) argues that large language model outputs should be viewed as model-specific idiolects rather than a collective "AI language," based on analysis of 2024 and 2026 corpora of six models each. The study found a generational style shift between cohorts while each model maintained a unique linguistic profile, with contraction frequencies varying from over 1,200 to over 30,000 per million words within the 2026 cohort.

arXiv:2608.06589v1 Announce Type: new Abstract: While large language model outputs are frequently analysed as a collective super variety termed "AI language," this chapter argues that this perspective coexists with distinct, model-specific linguistic signatures akin to human idiolects. We analyse two datasets of LLM-generated texts on societal topics: a 2024 corpus of six models Improta et al. 2024 and a newly generated 2026 corpus using the same prompts featuring six contemporary models. Our findings, utilising computational descriptors and stylometric principal component analysis reveal a generational shift between the style of the 2024 and 2026 cohorts, while demonstrating that each individual model maintains a unique linguistic profile. This multi-layered interplay is illustrated by contraction frequencies, which vary from over 1,200 to over 30,000 per million words within the same cohort of models 2026 . Ultimately, we conclude that treating LLM output as idiolectal in nature provides a valuable framework with potential implications for research on variation and change, LLM-generated text detection, forensic linguistics and usage-based approaches to language.