@inproceedings{teixeira-etal-2026-math,
    title = "{MATH}-{PT}: A Math Reasoning Benchmark for {E}uropean and {B}razilian {P}ortuguese",
    author = "Teixeira, Tiago  and
      Erthal, Ana Carolina  and
      Belieni, Juan  and
      Canaverde, Beatriz  and
      Mesquita, Diego  and
      Faria, Miguel  and
      Silva, Eliezer de Souza da  and
      Martins, Andr{\'e} F. T.",
    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-1.102/",
    pages = "1005--1010",
    ISBN = "979-8-89176-387-6",
    abstract = "The use of large language models (LLMs) for complex mathematical reasoning is an emergent area of research, with fast progress in methods, models, and benchmark datasets. However, most mathematical reasoning evaluations exhibit a significant linguistic bias, with the vast majority of benchmark datasets being exclusively in English or (at best) translated from English. We address this limitation by introducing MATH-PT, a novel dataset comprising 1,729 mathematical problems written in European and Brazilian Portuguese. MATH-PT is curated from a variety of high-quality native sources, including mathematical Olympiads, competitions, and exams from Portugal and Brazil. We present a comprehensive benchmark of current state-of-the-art LLMs on MATHPT, revealing that frontier reasoning models achieve strong performance in multiple choice questions compared to open weight models, but that their performance decreases for questions with figures or open-ended questions. To facilitate future research, we release the benchmark dataset and model outputs"
}