arXiv:2605.22705v1 Announce Type: new Abstract: We introduce Tokenization with Split Trees (ToaST), a subword tokenization method that directly optimizes compression under a new recursive inference procedure. ToaST greedily splits each pretoken into a full binary tree using precomputed byte n-gram counts, independent of any vocabulary. Given a vocabulary, inference recursively descends each split tree and emits the first in-vocabulary node reached on each path. Vocabulary selection is formulated as an Integer Program (IP) that minimizes the total token count over all split trees under this inf
The paper introduces a novel tokenization method, ToaST, which directly optimizes compression and improves subword tokenization, addressing a foundational challenge in natural language processing (NLP). This development comes as the demand for efficient and effective tokenization grows with increasingly large language models.
Improved tokenization can lead to more efficient and accurate AI models, reducing computational overhead and potentially enhancing performance across various NLP applications. This development could subtly, but significantly, improve the underlying infrastructure for AI systems.
The introduction of ToaST offers an alternative, potentially more efficient, and independent tokenization approach not reliant on fixed vocabularies or complex inference, which could change how AI models are trained and deployed.
- · AI developers
- · NLP researchers
- · Cloud computing providers
- · Existing tokenization methods
- · Models reliant on less efficient tokenization
More compact and performant language models become possible due to superior tokenization.
Reduced computational costs for training and inference, making AI more accessible and energy efficient.
Enhanced AI applications across various industries as models become more precise and less resource-intensive.
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