This work reveals issues with token inflation in Arabic language models, highlighting the benefits of structured embedding through root-pattern tensor subspaces.
In our paper, we explore the limitations of byte pair encoding (BPE) in Arabic language models, revealing significant token inflation and a failure to preserve morphological relationships. We introduce root pattern tensor subspaces (RPTS) as a more efficient and linguistically informed alternative, achieving a remarkable reduction in embedding parameters while maintaining structural integrity.
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Salhi arselen (2026) studied this question.
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