AI Research
The Price of Token Boundaries: Compression Certificates and Prediction
Uncovering the hidden efficiency costs inherent in standard machine learning tokenization practices.
Pre-tokenisation restricts which text fragments can become prediction units, but its compression cost is obscured when tokenisers are compared only under the same boundaries. We measure this cost by bounding the minimum token count from both sides, with and without a regular-expr...
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ArXiv CS.AI
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ArXiv CS.AI
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Current tokenization methods impose a hidden computational tax by forcing models to process text through rigid, pre-defined boundaries. By quantifying this compression cost, researchers can better optimize architecture efficiency and improve predictive performance across diverse linguistic structures.
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