There is No Theoretical Curse of Multilinguality For Embedding Space Structure
Theoretical proof suggests multilingual model degradation stems from data limitations, not architectural barriers.
A central goal of multilingual NLP is to achieve high monolingual performance per language and cross-lingual alignment for large-scale language coverage with a multilingual model. The curse of multilinguality describes the phenomenon of degradation in multilingual model performan...
ArXiv CS.CL
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This research provides a fundamental theoretical breakthrough by proving that the limits of multilingual models are driven by data constraints rather than inherent mathematical impossibility. By decoupling model capacity from language quantity, it offers a new roadmap for developing more efficient global AI systems.
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