@inproceedings{nobre-etal-2026-language,
    title = "Language Effects in Text-to-{SQL} Across {E}nglish and {P}ortuguese",
    author = "Nobre, Lucas  and
      Sousa, Suele  and
      Teles, Savio  and
      Soares, Anderson",
    editor = "Souza, Marlo  and
      de-Dios-Flores, Iria  and
      Santos, Diana  and
      Freitas, Larissa  and
      Souza, Jackson Wilke da Cruz  and
      Ribeiro, Eug{\'e}nio",
    booktitle = "Proceedings of the 17th International Conference on Computational Processing of {P}ortuguese ({PROPOR} 2026) - Vol. 1",
    month = apr,
    year = "2026",
    address = "Salvador, Brazil",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-dnd/2026.propor-1.27/",
    pages = "270--280",
    ISBN = "979-8-89176-387-6",
    abstract = "Text-to-SQL systems allow users to query relational databases using natural language, but accuracy remains sensitive to the choice of language, model architecture, and prompting strategy. Although recent Large Language Models (LLMs) incorporate reasoning mechanisms that improve multi-step problem solving in other domains, their effects on multilingual Text-to-SQL are not yet well understood. This work evaluates a diverse set of LLMs on the BIRD benchmark and BIRD{\_}PT, a Portuguese version produced by translating the questions and external knowledge while keeping the original English database schema and values unchanged. We compare four controlled scenarios that vary internal reasoning and guided reasoning for SQL generation. The results show a consistent decrease in accuracy when switching from English to Portuguese, with large variations in robustness across models. Reasoning alone does not reliably improve execution accuracy and can reduce performance in Portuguese, while combining reasoning with a guided plan provides the most stable improvements, although still weaker than in English. These findings highlight ongoing challenges in multilingual Text-to-SQL and emphasize the need to jointly consider language understanding, reasoning activation, and task-aligned planning when designing future systems."
}