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This research investigates the limitations of tokenization strategies in Arabic language models and proposes an alternative.

March 30, 2026Open Access

Tokenization Induced Morphological Degradation in Arabic Language Models and a Structured Embedding Alternative: Root-Pattern Tensor Subspaces

Key Points

This research investigates the limitations of tokenization strategies in Arabic language models and proposes an alternative.
Analyzed byte pair encoding limitations in Arabic models.
Introduced root pattern tensor subspaces as an alternative method for embedding.
Evaluated the efficiency of embedding parameters with the new model.
Revealed significant token inflation and morphological degradation with byte pair encoding.
Achieved reduced embedding parameters with root pattern tensor subspaces while maintaining structural integrity.

Abstract

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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