arXiv:2604.17633v2 Announce Type: replace Abstract: Large language models exhibit impressive cross-lingual capabilities. However, prior work analyzes this phenomenon through isolated factors and at sparse points during training, limiting our understanding of how cross-lingual generalization emerges--particularly in the early phases of learning. To study the early trajectory of linguistic and translation capabilities, we pretrain a multilingual 1.7B model on nine diverse languages, capturing checkpoints at a much finer granularity. We use word-level translation as a testbed, introducing a novel
This research provides a deeper, fine-grained understanding of how multilingual capabilities and cross-lingual generalization emerge in large language models during early training phases.
A strategic reader needs to understand the fundamental mechanisms of multilingual AI to better predict its evolution, capabilities, and the implications for global information flow and digital sovereignty.
This research shifts our understanding from observing isolated factors to a more dynamic, temporal view of linguistic and translation emergence in AI, highlighting the early-stage learning process.
- · AI researchers and developers
- · Multilingual AI platforms
- · Global content creators
- · Monolingual content systems
- · AI models without robust cross-lingual capabilities
Improved architectures and training methodologies for multilingual large language models will accelerate their development.
More sophisticated and nuanced cross-lingual AI will reduce language barriers across various applications, enhancing global communication and commerce.
The deeper understanding of linguistic transfer in AI could inform the development of more generalized and human-like AI cognition, leading to breakthroughs in other AI domains.
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