# Towards Open Foundation Language Model and Corpus for Macedonian: A Low-Resource Language

arXiv: https://arxiv.org/abs/2506.09560 · HTML: https://mlsanity.com/paper/2506.09560/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 28 of its 51 references that are in the map.
- First posted on arXiv 11 Jun 2025.

## Builds on (most cited first, 28 of 28)

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- [The Pile: An 800GB Dataset of Diverse Text for Language Modeling](https://mlsanity.com/paper/2101.00027/) (2020)
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- [Can a Suit of Armor Conduct Electricity? A New Dataset for Open Book Question Answering](https://mlsanity.com/paper/1809.02789/) (2018)
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- [Systematic Inequalities in Language Technology Performance across the World's Languages](https://mlsanity.com/paper/2110.06733/) (2021)
- [Docling: An Efficient Open-Source Toolkit for AI-driven Document Conversion](https://mlsanity.com/paper/2501.17887/) (2025)
- [FinGPT: Large Generative Models for a Small Language](https://mlsanity.com/paper/2311.05640/) (2023)
- [BERTi'c -- The Transformer Language Model for Bosnian, Croatian, Montenegrin and Serbian](https://mlsanity.com/paper/2104.09243/) (2021)
- [FinEst BERT and CroSloEngual BERT: less is more in multilingual models](https://mlsanity.com/paper/2006.07890/) (2020)
- [TaCo: Enhancing Cross-Lingual Transfer for Low-Resource Languages in LLMs through Translation-Assisted Chain-of-Thought Processes](https://mlsanity.com/paper/2311.10797/) (2023)
- [An Expanded Massive Multilingual Dataset for High-Performance Language Technologies (HPLT)](https://mlsanity.com/paper/2503.10267/) (2025)

## Abstract (arXiv)

The increase in technological adoption worldwide comes with demands for novel tools to be used by the general population. Large Language Models (LLMs) provide a great opportunity in this respect, but their capabilities remain limited for low-resource languages, restricting applications in countries where such languages are spoken. We create several resources to facilitate the adoption of LLMs and to support research advancements for Macedonian. We collect the largest Macedonian corpus to date, consisting of 40GB of textual data and totaling 3.5B words. To support conversational applications, we collect a 106k-instance instruction dataset, carefully built to be culturally grounded. For evaluation, we construct a Macedonian evaluation suite covering seven benchmarks. Finally, we train domestic-yak, a state-of-the-art 8B-parameter model, on our curated datasets and evaluate it against eight baseline models using the newly constructed benchmark suite. Our model outperforms all existing models in the 8B parameter range across all benchmarks, and achieves performance comparable to models up to 10x larger. Furthermore, a qualitative analysis with native speakers reveals that our model is preferred over larger counterparts, receiving higher ratings for grammatical correctness and cultural appropriateness. All datasets, code, and model weights are openly released, setting a foundation for advancing LLMs in similarly underrepresented languages. These resources are publicly available at github.com/LVSTCK for source code, and at huggingface.co/LVSTCK for pretrained model weights and data.