From Zero to Hero: An Open LLM Ecosystem for Armenian

Researchers from COPA released an open Armenian language-model ecosystem comprising ArmWeb, a 3.3-billion-token curated news corpus with provenance, deduplication, and benchmark decontamination; ArmSTEM, 373,000 English-Armenian STEM problem pairs translated and verified through automated and human checks; and arm-gemma-e4b, a Gemma-4 model continued-pretrained for 10 billion tokens using the datasets. They report that arm-gemma-e4b achieves the strongest average results among compared open Armenian models on a six-task likelihood suite, particularly on knowledge-oriented benchmarks, and improves over its unadapted base model, while generative ArmBench results suggest that question-answer-style pretraining also improves strict-format task performance without instruction tuning. Controlled experiments found that news-only continued pretraining could cause substantial knowledge and reading-comprehension degradation despite English replay, whereas replacing part of the news data with verified STEM material reduced or reversed this forgetting effect. The project makes its datasets, pipelines, training recipes, evaluation configurations, and model publicly available, emphasizing auditability and reproducibility, while acknowledging limitations including a news-heavy corpus, single-seed experiments, lack of safety or instruction tuning, weaker part-of-speech and exam-mathematics performance, and unresolved questions about whether STEM content, answer formatting, or verification drives the observed gains.

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