@inproceedings{iyer-etal-2024-exploring,
    title = "Exploring Very Low-Resource Translation with {LLM}s: The {U}niversity of {E}dinburgh`s Submission to {A}mericas{NLP} 2024 Translation Task",
    author = "Iyer, Vivek  and
      Malik, Bhavitvya  and
      Zhu, Wenhao  and
      Stepachev, Pavel  and
      Chen, Pinzhen  and
      Haddow, Barry  and
      Birch, Alexandra",
    editor = "Mager, Manuel  and
      Ebrahimi, Abteen  and
      Rijhwani, Shruti  and
      Oncevay, Arturo  and
      Chiruzzo, Luis  and
      Pugh, Robert  and
      von der Wense, Katharina",
    booktitle = "Proceedings of the 4th Workshop on Natural Language Processing for Indigenous Languages of the Americas (AmericasNLP 2024)",
    month = jun,
    year = "2024",
    address = "Mexico City, Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest_wac_2008/2024.americasnlp-1.25/",
    doi = "10.18653/v1/2024.americasnlp-1.25",
    pages = "209--220",
    abstract = "This paper describes the University of Edinburgh`s submission to the AmericasNLP 2024 shared task on the translation of Spanish into 11 indigenous American languages. We explore the ability of multilingual Large Language Models (LLMs) to model low-resource languages by continued pre-training with LoRA, and conduct instruction fine-tuning using a variety of datasets, demonstrating that this improves LLM performance. Furthermore, we demonstrate the efficacy of checkpoint averaging alongside decoding techniques like beam search and sampling, resulting in further improvements. We participate in all 11 translation directions."
}