BYOL: Bring Your Own Language Into LLMs

• URL: http://arxiv.org/abs/2601.10804v1
• Date: Thu, 15 Jan 2026 19:15:13 GMT
• Title: BYOL: Bring Your Own Language Into LLMs
• Authors: Syed Waqas Zamir, Wassim Hamidouche, Boulbaba Ben Amor, Luana Marotti, Inbal Becker-Reshef, Juan Lavista Ferres,
• Abstract summary: Large Language Models (LLMs) exhibit strong multilingual capabilities, yet remain constrained by the severe imbalance in global language resources.<n>This disparity leads to systematic underperformance, cultural misalignment, and limited accessibility for speakers of low-resource and extreme-low-resource languages.<n>We introduce Bring Your Own Language (BYOL), a unified framework for scalable, language-aware LLM development tailored to each language's digital footprint.
• Score: 12.151176703151428
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large Language Models (LLMs) exhibit strong multilingual capabilities, yet remain fundamentally constrained by the severe imbalance in global language resources. While over 7,000 languages are spoken worldwide, only a small subset (fewer than 100) has sufficient digital presence to meaningfully influence modern LLM training. This disparity leads to systematic underperformance, cultural misalignment, and limited accessibility for speakers of low-resource and extreme-low-resource languages. To address this gap, we introduce Bring Your Own Language (BYOL), a unified framework for scalable, language-aware LLM development tailored to each language's digital footprint. BYOL begins with a language resource classification that maps languages into four tiers (Extreme-Low, Low, Mid, High) using curated web-scale corpora, and uses this classification to select the appropriate integration pathway. For low-resource languages, we propose a full-stack data refinement and expansion pipeline that combines corpus cleaning, synthetic text generation, continual pretraining, and supervised finetuning. Applied to Chichewa and Maori, this pipeline yields language-specific LLMs that achieve approximately 12 percent average improvement over strong multilingual baselines across 12 benchmarks, while preserving English and multilingual capabilities via weight-space model merging. For extreme-low-resource languages, we introduce a translation-mediated inclusion pathway, and show on Inuktitut that a tailored machine translation system improves over a commercial baseline by 4 BLEU, enabling high-accuracy LLM access when direct language modeling is infeasible. Finally, we release human-translated versions of the Global MMLU-Lite benchmark in Chichewa, Maori, and Inuktitut, and make our codebase and models publicly available at https://github.com/microsoft/byol .

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