@inproceedings{guerra-2025-leveraging,
    title = "Leveraging {LLM}s for Cross-Locale Adaptation: a Workflow Proposal on {S}panish Variants",
    author = "Guerra, Vera Senderowicz",
    editor = {Bouillon, Pierrette  and
      Gerlach, Johanna  and
      Girletti, Sabrina  and
      Volkart, Lise  and
      Rubino, Raphael  and
      Sennrich, Rico  and
      L{\"a}ubli, Samuel  and
      Volk, Martin  and
      Espl{\`a}-Gomis, Miquel  and
      Vandeghinste, Vincent  and
      Moniz, Helena  and
      Szoc, Sara},
    booktitle = "Proceedings of Machine Translation Summit XX: Volume 2",
    month = jun,
    year = "2025",
    address = "Geneva, Switzerland",
    publisher = "European Association for Machine Translation",
    url = "https://preview.aclanthology.org/mtsummit-25-ingestion/2025.mtsummit-2.7/",
    pages = "58--66",
    ISBN = "978-2-9701897-1-8",
    abstract = "Localization strategies can differ widely between languages, but the necessity and efficiency of maintaining distinct strategies for closely related variants of the same language is debatable. This paper explores the potential for unifying localization strategies across different Spanish locales, leveraging Large Language Models, prompting techniques, and specialized linguistic resources to perform cross-locale adaptations from a chosen baseline. In this study, we examine and develop vocabulary, terminology, grammar, and style transformation methods from Latin American into Mexican and Argentine Spanish. Our findings suggest that parting from a core translation and then following an automated adaptation process to unify localization strategies is feasible for Spanish diverse variants, regardless of the type of divergence each of them has from the baseline locale. However, even if the need for human post-editing is then minimal compared to a fully `manual' cross-locale adaptation, the linguistic review remains crucial, particularly for editing style nuances."
}