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 {\sc Math-PT}, a novel dataset comprising 1,729 mathematical problems written in European and Brazilian Portuguese. {\sc 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 {\sc Math-PT}, 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.
| Comments: | Accepted at 17th International Conference on Computational Processing of Portuguese (PROPOR 2026). Open access to dataset repo this https URL and model outputs this https URL |
| Subjects: | Computation and Language (cs.CL); Information Retrieval (cs.IR) |
| ACM classes: | I.2.7; I.2.0 |
| Cite as: | arXiv:2604.25926 [cs.CL] |
| (or arXiv:2604.25926v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2604.25926 arXiv-issued DOI via DataCite |
Submission history
From: Eliezer De Souza Da Silva [view email]
[v1]
Wed, 1 Apr 2026 12:12:54 UTC (119 KB)