MinGram: A Minimalist Unigram Tokenizer with High Compression and Competitive Morphological Alignment
arXiv:2606.27019v1 Announce Type: new Abstract: The Unigram tokenizer uses an elegant representation which makes it straightforward to edit vocabularies, but its training is comparatively heavy and complex. We introduce MinGram (Minimalist Unigram), which keeps the token-list representation but simplifies training using a BPE-derived seed vocabulary, Hard EM on a minimum-token path, and a single flat score-pruning step. This removes the suffix array, the forward-backward pass, and the iterative prune loop, leaving a procedure that requires little beyond tokenizer inference itself. By making to
The continuous drive for more efficient and performant AI models necessitates ongoing innovation in fundamental components like tokenization, as AI capabilities advance rapidly.
Improved tokenization methods directly impact the training efficiency, performance, and potentially the computational cost of large language models, a key bottleneck for further AI development.
This new tokenizer simplifies the training process for Unigram models, potentially making them more accessible and efficient to implement compared to prior methods.
- · AI researchers
- · Large language model developers
- · Cloud AI providers
- · Less efficient tokenization methods
Easier and faster tokenization model development for new languages and domains.
Reduced computational resources required for preprocessing and training of certain NLP models, potentially lowering overall inference costs.
Broader adoption of Unigram-based tokenizers due to simplified training, leading to more consistent and potentially better morphological alignment across different models.
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