@InProceedings{ingason-mechler-stefnsdttir:2026:latell,
  author    = {Ingason, Anton Karl  and  Mechler, Johanna  and  Stefánsdóttir, Lilja Björk},
  title     = {Do Large Language Models Style-Shift? Register-Conditioned Stylistic Fronting in AI-Generated Icelandic},
  booktitle      = {Proceedings of the First International Conference on Language Technologies for Low-resource Languages (LaTeLL 2026)},
  month          = {September},
  year           = {2026},
  address        = {Fes, Morocco},
  publisher      = {Association for Computational Linguistics},
  pages     = {140--149},
  abstract  = {We examine whether large language models (LLM) encode sociolinguistic knowledge of formal and informal register in a low-resource language, Icelandic, finding that register sensitivity is present in the two systems tested (GPT-5.5 and Gemini 3.1 Pro). Our findings are based on a 2×2 persona design with elicited production (n=1,200), manually coded for Stylistic Fronting, a property of formal language. The direction and size of their style shift are not detectably different, but they differ sharply in overall rate; against a matched human comparator, only GPT-5.5 produces a persona-appropriate rate. Neither system reproduces the gender pattern documented for human speakers. Thus, register sensitivity does not guarantee that output rates are appropriate to the persona the model was instructed to adopt, an issue that may reflect limited training data for low-resource languages.},
  url       = {https://aclanthology.org/2026.latell-1.16}
}