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Matthias Lindemann

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#machine learning Preprint Oct 2026

Length Generalization Needs Proper Regularization

Length generalization is the ability of sequential models to perform well on context lengths unseen during training. In this work, we show that the challenge of achieving length generalization is related not only to architectural choices such as positional encoding and the attention mechanism but also to the training p...

Pavlo Vasylenko, Matthias Lindemann, André F. T. Martins et al. · 0 citations

MultiSynt/MT: Trillion-Token Multi-Parallel Pre-Training Data Translated Across 36 Languages

This work introduces MultiSynt/MT, an open synthetic parallel corpus with approximately 4.8 trillion target-language tokens across 36 languages, produced by translating 100 billion high-quality Nemotron-CC tokens with Tower+ and OPUS-MT/HPLT-MT systems, and releases the corpus to support controlled research on multilin...

Maximilian Idahl, Jörg Tiedemann, S. Pyysalo et al. · 0 citations

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