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Multilingual-Math-20K (EN / ZH / ES / TH)
19,759 math reasoning problems, fully parallel across four languages (English original + Chinese / Spanish / Thai translations), each with a step-by-step solution ending in a unified \boxed{<answer>} and a gold answer field.
- 79,036 problem–language pairs (19,759 × 4), aligned by
qid - Solutions included for every language (not just questions)
- Per-record provenance: source dataset and subset
Composition
| Source | Records | Notes |
|---|---|---|
| GSM8K (train) | 7,473 | grade-school word problems; #### N answers converted to \boxed{N} |
| OpenR1-Math-220k (sampled) | 12,286 | olympiads 9,322 · cn_contest 1,115 · aops_forum 1,016 · amc_aime 715 · inequalities 80 · number_theory 38 |
Answer types: numeric 16,682 · letter 2,718 · LaTeX 236 · other 123.
Summary statistics in stats.json.
Data fields
One JSON object per line; file mixed_20k_<lang>.jsonl carries question_<lang> / solution_<lang>:
| Field | Description |
|---|---|
qid |
Stable problem id, identical across the four files (parallel alignment key) |
source / subset |
gsm8k or openr1 + fine-grained subset |
problem_type, openr1_gid, openr1_cc |
OpenR1 provenance metadata (null for GSM8K) |
answer |
Gold final answer; for OpenR1 extracted from the last \boxed{} of the English solution, for GSM8K the original label |
question_<lang> |
Problem statement in the file's language |
solution_<lang> |
Step-by-step solution in the file's language, ending in \boxed{<answer>} |
Translation
The Chinese, Spanish and Thai versions were machine-translated with DeepSeek models. Records that failed automatic language-identification or answer-consistency checks were re-translated, and a small number were corrected by hand.
Intended uses
Research on multilingual mathematical reasoning: SFT / RL post-training, studying response-language behavior (e.g., whether models reason in the query's language), translated-data ablations, and cross-lingual evaluation. The four-way parallel structure (same qid across languages) supports controlled comparisons of the same problem across languages.
Limitations
- Translations are machine-generated; residual translation noise exists, most likely in Thai.
- The OpenR1 portion skews toward competition-level problems; the GSM8K portion is grade-school level. Filter by
source/subsetif you need a uniform difficulty band. - ~15% of answers are non-numeric (letter/LaTeX); filter by answer type for exact-match numeric evaluation.
- Only questions and solutions were translated; no cultural localization was performed (names, currencies, and contexts remain as in the originals).
Licensing and attribution
Released under Apache-2.0, consistent with the upstream licenses: GSM8K (MIT, OpenAI) and OpenR1-Math-220k (Apache-2.0, Hugging Face). If you use this dataset, please also credit the upstream datasets.
Citation
@inproceedings{mgoal2026,
title = {M-GOAL: Toward Accurate and Language-Consistent Reasoning in LLMs via English-Only Post-Training},
booktitle = {Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing (EMNLP)},
year = {2026},
note = {To appear}
}
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