@inproceedings{xiao-etal-2026-math,
    title = "{MATH}-{IDN}: A Multilingual Mathematical Problem Solving Dataset Featuring Local Languages in {I}ndonesia",
    author = "Xiao, Xiao  and
      Ni'mah, Iftitahu  and
      Wabula, Yuyun  and
      Pechenizkiy, Mykola  and
      Fang, Meng",
    editor = "Demberg, Vera  and
      Inui, Kentaro  and
      Marquez, Llu{\'i}s",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EACL} 2026",
    month = mar,
    year = "2026",
    address = "Rabat, Morocco",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-eacl/2026.findings-eacl.231/",
    pages = "4432--4438",
    ISBN = "979-8-89176-386-9",
    abstract = "Large Language Models (LLMs) excel at mathematical reasoning in English, but their performance in low-resource languages remains underexplored. This gap is particularly critical in the Indonesian context, where equitable access to AI systems depends on robust multilingual reasoning across diverse local languages.We introduce MATH-IDN, a multilingual benchmark for mathematical problem solving in Indonesian, Javanese, Sundanese, and Buginese, with English as a reference, following the MATH dataset. We evaluate multiple open-source LLMs, including math-specialized, Southeast-Asian-adapted, and general-purpose models, under a zero-shot chain-of-thought setting. Results show that MATH-IDN presents a challenging and discriminative benchmark, revealing substantial performance gaps in low-resource languages, particularly Buginese, and highlighting key limitations in current multilingual reasoning capabilities. Our data and code are available at https://github.com/aialt/MATH-IND."
}