@inproceedings{pocco-oncevay-2026-robustness,
    title = "On the Robustness of Morphosyntactic Transformation with Large Language Models: The Case of {Q}uechua Collao",
    author = "Pocco, Pool  and
      Oncevay, Arturo",
    editor = "Mager, Manuel  and
      Ebrahimi, Abteen  and
      Bui, Minh Duc  and
      Pugh, Robert  and
      Oncevay, Arturo  and
      Chiruzzo, Luis  and
      Solano, Rolando Coto  and
      Rijhwani, Shruti  and
      Von Der Wense, Katharina",
    booktitle = "Proceedings of the Sixth Workshop on {NLP} for Indigenous Languages of the {A}mericas ({A}mericas{NLP})",
    month = jul,
    year = "2026",
    address = "San Diego, California, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl-workshops/2026.americasnlp-6.12/",
    pages = "128--146",
    ISBN = "979-8-89176-415-6",
    abstract = "We present a morphosyntactically controlled transformation dataset for Quechua Collao and evaluate large language models on a sentence-level transformation task under varying prompting conditions. Results show that performance depends on the interaction between model behavior, context size, and linguistic complexity, with smaller models benefiting more from additional examples and morphological hints providing selective gains."
}