README.md

---
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.