| --- |
| license: cc-by-4.0 |
| language: |
| - hy |
| - en |
| task_categories: |
| - text-generation |
| - question-answering |
| tags: |
| - arxiv:2609.03350 |
| - armenian |
| - math |
| - science |
| - reasoning |
| - machine-translated |
| - verified |
| - parallel-corpus |
| size_categories: |
| - 100K<n<1M |
| pretty_name: ArmSTEM |
| configs: |
| - config_name: default |
| data_files: |
| - split: train |
| path: data/train/* |
| --- |
| |
| # ArmSTEM |
|
|
| **ArmSTEM** is a corpus of **373K verified machine-translated mathematics and |
| science problems**, 324K of them with step-by-step solutions, released as |
| **parallel English–Armenian** (~311M Armenian + ~124M English tokens, Gemma tokenizer). To our knowledge it is the first Armenian STEM corpus with |
| worked solutions at training scale. |
|
|
| Every item passed a verification pipeline whose central gate is **blind |
| re-solving**: an independent model solves the *Armenian* problem and must |
| reproduce the original gold answer exactly. Translation preserved not just |
| fluency but *correctness*. |
|
|
| In our experiments (see paper), mixing just 6% ArmSTEM into Armenian continued |
| pretraining **reversed catastrophic forgetting** — the adapted model ended |
| *above* its base on a six-task Armenian suite (+9.7pp on Belebele) instead of |
| 21 points below it. |
|
|
| Sister releases: [COPA-AI/armweb](https://huggingface.co/datasets/COPA-AI/armweb) |
| and [COPA-AI/arm-gemma-e4b](https://huggingface.co/COPA-AI/arm-gemma-e4b). |
|
|
| ## Composition (v1) |
|
|
| | Source (English) | License | Items accepted | Accept rate | |
| |---|---|---|---| |
| | [GSM8K](https://huggingface.co/datasets/openai/gsm8k) | MIT | 7,404 | 99.1% | |
| | [AceReason-Math](https://huggingface.co/datasets/nvidia/AceReason-Math) | CC-BY-4.0 | 48,584 | 98.0% | |
| | [OpenScience](https://huggingface.co/datasets/nvidia/OpenScience) | CC-BY-4.0 | 264,266 | 97.4% | |
| | [OpenScienceReasoning-2](https://huggingface.co/datasets/nvidia/OpenScienceReasoning-2) | CC-BY-4.0 | 52,653 | 91.5% | |
|
|
| Translation of the full source pools is complete (372,907 unique verified |
| pairs). AceReason-Math provides final answers without worked solutions, so |
| its 48,584 items carry `cot_en` = null and `cot_hy` = the answer; the other |
| 324,323 items include step-by-step solutions on both sides. An automated adequacy audit (GPT-5.5, 300 stratified items, 1–5 |
| scale) rates 100% of re-solve-verified and 92.7% of solver-limited items |
| as meaning-preserving (means 4.65 / 4.39); the audit file ships in |
| `stats/`. The audit judge (GPT-5.5) also serves as the escalation |
| translator for a small fraction of items. For a complete verification of |
| the translated samples, two native Armenian speakers independently assessed |
| a 300-item sample for logical coherence and solution correctness, rating |
| 299/300 valid with identical verdicts on every item (Cohen's κ = 1.0). Blind re-solve marks 10.9% of accepted math and 27.6% of science |
| items `solver_limited` (the English original also resists the solver, so |
| the gate certifies problem-statement integrity only). A harder |
| competition-math tranche will ship as v1.1. |
|
|
| **Fields**: `id`, `src` (source dataset), `question_en`, `cot_en` (solution; |
| null for AceReason-Math), `question_hy`, `cot_hy`, `gold`, `answer_type` (`numeric`/`mc`/`freeform`), |
| `solver_limited` (bool, see gate G2 below), `translator`, `attempts`. |
| Rejected items and per-stage rejection statistics ship in `stats/`, together |
| with `stats/cpt_training_subset_ids.txt`, the 104,630 ids of the CPT |
| training subset that appear verbatim in this release (the remaining 5,255 |
| OpenScience items of that subset were superseded by revised translations |
| before release). |
|
|
| ## Pipeline |
|
|
| 1. **English-side decontamination**: 13-gram scan against English benchmark |
| origins (above all MMLU-Pro test, the source of ArmBench's largest |
| column). Contaminated items are dropped *before* translation. |
| 2. **Placeholder masking**: numbers, LaTeX spans, and the question/solution |
| separator are replaced with indexed placeholder tokens (⟦N1⟧, ⟦EQ2⟧, …) |
| before translation and restored afterward — number/notation corruption, |
| the dominant MT failure mode for math, is eliminated structurally. |
| 3. **Translation**: Gemini-3.1-flash-lite (selected over GPT-5.5 by a |
| fixed-protocol 200-item A/B), with two feedback-guided repair rounds and |
| escalation to a stronger translator on repeated failure. |
| 4. **Gates**: G0 placeholder integrity (every token exactly once, no stray |
| digits) → G1 language ID (GlotLID) → **G2 blind re-solve**: o4-mini solves |
| the Armenian problem; the final answer must match the gold exactly |
| (numeric/MC). On mismatch, a control re-solves the *English* original — if |
| that also fails, the item is solver-limited, not mistranslated, and is |
| kept with a `solver_limited` tag. Freeform answers (<5%) use a three-model |
| judge panel, 2/3 majority. |
| 5. **Armenian-side decontamination**: accepted translations are scanned |
| against the full ArmBench-LLM item set (both gates rejected real |
| collisions in production). |
|
|
| Per-stage rejection statistics ship with the corpus (`stats/`). |
|
|
| ## License, attribution, statement of changes |
|
|
| Each subset inherits its source license (MIT for GSM8K; CC-BY-4.0 for the |
| NVIDIA-released sets). Per the CC-BY requirements: the material was |
| **machine-translated to Eastern Armenian with automated verification**; |
| original sources are credited above and per-item in the `src` field. |
| OpenScience/OpenScienceReasoning-2 are Qwen-generated synthetic data: the |
| data license is CC-BY-4.0, and per the upstream note, *models trained on |
| them* may inherit Qwen-license considerations. |
|
|
| **Decontamination notice**: ArmSTEM is decontaminated against both its |
| English benchmark origins and Armenian targets (ArmBench-LLM, including |
| MMLU-Pro-Hy). It is safe to train on and evaluate on those benchmarks. |
| m-MMLU-hy and ARC-hy were not decontamination targets; a post-hoc 13-gram |
| scan found 4 of the 372,907 pairs sharing a 13-gram with their items. |
|
|
| ## Citation |
|
|
| ```bibtex |
| @article{arakelyan2026armweb, |
| title = {From Zero to Hero: An Open LLM Ecosystem for Armenian}, |
| author = {Arakelyan, Erik and Avetisyan, Khatun and Davtyan, Meri and Grigoryan, Heghine and Khachatryan, Nane and Shahsuvaryan, Hayk and Sergoyan, Henrik and Martirosyan, Vahan}, |
| year = {2026}, |
| journal = {arXiv preprint arXiv:2609.03350} |
| } |
| ``` |
|
|
| Please also cite the source datasets (GSM8K, AceReason-Math, OpenScience, |
| OpenScienceReasoning-2) when using the corresponding subsets. |
|
|
