--- language: - en - eu license: mit task_categories: - text-generation - question-answering tags: - math - word-problems - maseu - mawps - asdiv_a - svamp - gsm8k - mgsm - multilingual - basque - low-resource pretty_name: MASEU dataset_info: features: - name: Question dtype: string - name: Numbers sequence: float64 - name: Equation sequence: string - name: Answer dtype: float64 - name: group_nums sequence: int64 - name: Body dtype: string - name: Ques dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 118560 num_examples: 195 - name: test num_bytes: 816875 num_examples: 1584 download_size: 277198 dataset_size: 935435 configs: - config_name: en data_files: - split: train path: en/train-* - split: test path: en/test-* - config_name: eu data_files: - split: train path: eu/train-* - split: test path: eu/test-* --- # MASEU Multilingual ## Dataset Description MASEU is a dataset specifically constructed to enable reliable and linguistically faithful evaluation of mathematical reasoning in Basque, a low-resource language. It is based on a manually curated subset of the [mawps-asdiv-a_svamp](https://huggingface.co/datasets/ChilleD/SVAMP) corpus, which merges three well-established benchmarks in the domain of Math Word Problems (MWPs): [MAWPS](https://aclanthology.org/N16-1136/), [ASDiv-A](https://aclanthology.org/2020.findings-emnlp.195/), and [SVAMP](https://aclanthology.org/2021.naacl-main.168/). These datasets were selected for their diversity in reasoning types, consistent structure, and pedagogical value, making them particularly suitable for testing LLM performance in multilingual and instructional contexts. The Basque portion of MASEU comprises 195 train entries and 1584 test entries, all carefully translated into Basque by a single native speaker, without the use of any machine translation tools or automated assistance. The translation process was fully manual and carefully controlled to ensure both mathematical fidelity and linguistic naturalness, faithfully preserving the original intent, difficulty level, and logical structure of each problem. This guarantees that the Basque version reflects idiomatic usage while maintaining conceptual equivalence, enabling robust reasoning evaluation without introducing semantic drift. ## Available Configs | Config | Language | |--------|----------| | `en` | English | | `eu` | Basque | ## Splits | Split | Examples | Description | |---------|----------|------------------------------------| | `train` | 195 | Training set | | `test` | 1584 | Test set for evaluation | ## Usage ```python from datasets import load_dataset # Load the English config dataset = load_dataset("inigomartinez/MASEU", name="en") # Load the Basque config dataset = load_dataset("inigomartinez/MASEU", name="eu") # Access the splits train = dataset["train"] test = dataset["test"] ``` ## Data Fields | Field | Type | Description | |--------------|--------------------|----------------------------------------------------| | `id` | `int64` | Unique identifier for the example | | `Question` | `string` | Full math problem in natural language | | `Body` | `string` | Body of the problem statement | | `Ques` | `string` | Specific question of the problem | | `Numbers` | `sequence float64` | List of relevant numbers present in the problem | | `Equation` | `sequence string` | Equation(s) that solve the problem | | `Answer` | `float64` | Final numerical answer | | `group_nums` | `sequence int64` | Grouping of the numbers in the problem | ## Format The data is stored in **Parquet** format. ## Citation ```bibtex @inproceedings{martinezcriado-etal-2026-benchmarking, title = {Benchmarking Mathematical Reasoning in a Low-Resource Language: Structured Prompting and Evaluation in Basque}, author = {Martinez-Criado, Inigo and Soroa, Aitor and Barnes, Jeremy}, booktitle = {Proceedings of the Fifteenth Language Resources and Evaluation Conference (LREC 2026)}, month = {May}, year = {2026}, pages = {5268--5289}, address = {Palma, Mallorca, Spain}, publisher = {European Language Resources Association (ELRA)}, editor = {Piperidis, Stelios and Bel, Núria and van den Heuvel, Henk and Ide, Nancy and Krek, Simon and Toral, Antonio}, doi = {10.63317/48gd2ysjuh8s}, abstract = {Large Language Models (LLMs) have shown impressive performance on tasks requiring complex reasoning, but most evaluations tend to focus on English and other high-resource languages. This work investigates how well LLMs perform mathematical reasoning in low-resource languages, using Basque as a primary case study. To support this analysis, we introduce MASEU, a benchmark designed to evaluate reasoning in Basque across arithmetic, algebraic, and logical tasks. We then use this dataset to address three key questions: 1) how well do LLMs support Basque in reasoning tasks, 2) to what extent can including English in prompts improve results, and 3) what is the effect of continued pretraining in Basque? To explore these aspects, we use prompting strategies adapted for mathematical reasoning, building upon the foundations of CoT prompting and one of its subsequent evolutions, DUP prompting, which together allow for more precise experimentation across zero-shot and few-shot settings, providing insights into how multilingual models handle reasoning tasks in underrepresented languages.} } @inproceedings{koncel-kedziorski-etal-2016-mawps, title = {MAWPS: A Math Word Problem Repository}, author = {Koncel-Kedziorski, Rik and Roy, Subhro and Amini, Aida and Kushman, Nate and Hajishirzi, Hannaneh}, booktitle = {Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies}, year = {2016} } @inproceedings{miao-etal-2020-diverse, title = {A Diverse Corpus for Evaluating and Developing English Math Word Problem Solvers}, author = {Miao, Shen-yun and Liang, Chao-Chun and Su, Keh-Yih}, booktitle = {Findings of the Association for Computational Linguistics: EMNLP 2020}, year = {2020} } @inproceedings{patel-etal-2021-nlp, title = {Are NLP Models really able to Solve Simple Math Word Problems?}, author = {Patel, Arkil and Bhatt, Satwik and Baral, Chitta}, booktitle = {Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies}, year = {2021} } ```