MultiSynt/MT: Trillion-Token Multi-Parallel Pre-Training Data Translated Across 36 Languages
arXiv:2607.00890v1 Announce Type: new Abstract: Open web-scale pre-training corpora remain concentrated in English, limiting multilingual LLM development. We introduce MultiSynt/MT, an open synthetic parallel corpus with approximately 4.8 trillion target-language tokens across 36 European languages, produced by translating 100 billion high-quality Nemotron-CC tokens with Tower+ and OPUS-MT/HPLT-MT systems. For many medium- and lower-resource European languages, this is the largest openly available pre-training resource. On a broad multilingual benchmark suite, reference LLMs trained on MultiSy
The increasing demand for multilingual Large Language Models (LLMs) is driving the urgent need for diverse, non-English training data, which has historically been scarce.
This development significantly enhances the ability of non-English speaking nations and regions to develop performant LLMs, reducing dependency on English-centric models and tech stacks.
The availability of vast high-quality, multi-parallel pre-training data in 36 European languages will accelerate multilingual AI development and deployment outside of the current anglophone dominance.
- · European AI developers
- · Multilingual LLM companies
- · Medium- and lower-resource language communities
- · Open-source AI initiatives
- · Companies reliant solely on English-centric LLM development
- · Proprietary English dataset holders
Open-source LLMs will achieve significantly improved performance and cultural relevance in dozens of European languages.
This democratizes AI development, potentially leading to a fragmentation of leading LLMs along linguistic and national lines.
Enhanced LLM capabilities in sovereign languages could accelerate the adoption of AI in public services and highly regulated sectors within European nations, strengthening national AI capabilities.
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Read at arXiv cs.CL