@inproceedings{gupta-etal-2025-break,
    title = "Break-Ideate-Generate ({B}r{I}d{G}e): Moving beyond Translations for Localization using {LLM}s",
    author = "Gupta, Swapnil  and
      Carlini, Lucas Pereira  and
      Sircar, Prateek  and
      Gupta, Deepak",
    editor = "Chen, Weizhu  and
      Yang, Yi  and
      Kachuee, Mohammad  and
      Fu, Xue-Yong",
    booktitle = "Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 3: Industry Track)",
    month = apr,
    year = "2025",
    address = "Albuquerque, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2025.naacl-industry.51/",
    pages = "627--637",
    ISBN = "979-8-89176-194-0",
    abstract = "Language localization is the adaptation of written content to different linguistic and cultural contexts. Ability to localize written content is crucial for global businesses to provide consistent and reliable customer experience across diverse markets. Traditional methods have approached localization as an application of machine translation (MT), but localization requires more than linguistic conversion {--} content needs to align with the target audience{'}s cultural norms, linguistic nuances, and technical requirements. This difference is prominent for long-form text, where multiple facts are present in a creative choice of language. We propose a novel prompt approach for Large Languages Models (LLMs), called Break-Ideate-Generate (BrIdGe), for language localization. BrIdGe `breaks' the source content into granular facts, `ideates' an action plan for content creation in the target language by organizing the granular facts, and finally executes the plan to `generate' localized content. This approach emulates the cognitive processes humans employ in writing that begin with identifying important points, followed by brainstorming on how to structure and organize the output. We evaluated the BrIdGe methodology from multiple perspectives, including impact of BrIdGe prompt on different LLMs and performance comparisons with traditional MT models and direct translation through LLMs on public benchmark and proprietary e-commerce datasets. Through human and LLM-based automated evaluations across content in multiple languages, we demonstrate effectiveness of BrIdGe in generating fluent localized content while preserving factual consistency between source and target languages."
}