@inproceedings{shi-etal-2026-culturally,
    title = "Culturally-Aware Image Captioning for {G}uaran{\'i} with Multimodal Prompting: {IUH}oosiers at {A}mericas{NLP} 2026",
    author = "Shi, Wenchen  and
      Artkaew, Phakphum  and
      Gessler, Luke",
    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-6.21/",
    pages = "236--242",
    ISBN = "979-8-89176-415-6",
    abstract = "The AmericasNLP 2026 shared task challenges systems to generate culturally grounded image captions in indigenous languages of the Americas, a setting that demands both cultural awareness and linguistic accuracy for severely underresourced languages. We present IUHoosiers, Indiana University{'}s system for the Guaran{\'i} track. Rather than fine-tuning, our approach centers on inference-time knowledge injection: for each test image, we retrieve relevant Guaran{\'i} grammatical and cultural resources using BM25 and inject them into a large vision language model{'}s prompt alongside the image, enabling language-specific cultural and linguistic grounding without any parameter updates. IUHoosiers placed first for Guaran{\'i} in both automatic evaluation (24.67 chrF++) and human evaluation (3.45/5), outperforming all other participating systems."
}