@inproceedings{gagnier-kirubakaran-2026-deer,
    title = "Deer, Deities, and Dancing: Culturally Biased {LLM} Hallucination in Low-Resource Wix{\'a}rika Translation",
    author = "Gagnier, Henry  and
      Kirubakaran, Ashwin",
    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-6.7/",
    pages = "74--81",
    ISBN = "979-8-89176-415-6",
    abstract = "Large language models (LLMs) struggle with low-resource polysynthetic languages, yet the nature of their failures remains underexplored. We evaluate GPT-4o-mini, Gemma{\textasciitilde}3{\textasciitilde}27B, Llama{\textasciitilde}3.3{\textasciitilde}70B, and NLLB-200 on Spanish{\$}{\textbackslash}leftrightarrow{\$}Wix{\'a}rika translation using zero-shot and 5-shot prompting. All systems are unusable, scoring below 3 BLEU and 21 chrF. Qualitative analysis reveals that LLMs largely ignore source content and instead generate fluent hallucinations. Spanish outputs frequently include indigenous cultural stereotypes such as deer, deities, rain dance, and shamans, regardless of the input, while Wix{\'a}rika outputs are repetitive across different inputs and morphologically implausible. Few-shot prompting yields model-dependent improvements, with Gemma and Llama improving substantially at higher shot counts while GPT-4o-mini remains flat. These results demonstrate that current LLMs are unable to represent polysynthetic morphology and instead default to exoticizing Indigenous culture and identity. We call for the development of inclusive morphological-aware modeling strategies and increased resource creation to ensure that Indigenous languages of the Americas are represented safely and accurately."
}