Work

MATH-IDN: A Multilingual Mathematical Problem Solving Dataset Featuring Local Languages in Indonesia

Year: 2026

Type: conference-paper

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 lowresource languages, particularly Buginese, and highlighting key limitations in current multilingual reasoning capabilities.1

Source: Findings of the Association for Computational Linguistics: EACL

DOI: 10.18653/v1/2026.findings-eacl.231

Authors: Xiao Xiao, Iftitahu Ni'mah, Yuyun Wabula, Mykola Pechenizkiy, Meng Fang

Institutions: University of Liverpool, National Research and Innovation Agency, Eindhoven University of Technology

Language: English

Topic: Topic Modeling

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

OpenAlex ID: W7140077493

FWCI:

Cites:

Cited by:

Related to: 10