MinGram is a simplified Unigram tokenizer training method that prioritizes token count minimization to deliver higher compression than BPE and standard Unigram while retaining competitive morphological alignment and superior bits-per-byte performance in language model training.
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2026 2verdicts
UNVERDICTED 2representative citing papers
IPA-based subword tokenizers trained across 24 languages improve tokenization quality and generalization to unseen languages compared to standard text tokenizers, especially for non-Latin scripts.
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MinGram: A Minimalist Unigram Tokenizer with High Compression and Competitive Morphological Alignment
MinGram is a simplified Unigram tokenizer training method that prioritizes token count minimization to deliver higher compression than BPE and standard Unigram while retaining competitive morphological alignment and superior bits-per-byte performance in language model training.
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Phonemes to the Rescue: Multilingual Tokenization Based on International Phonetic Alphabet
IPA-based subword tokenizers trained across 24 languages improve tokenization quality and generalization to unseen languages compared to standard text tokenizers, especially for non-Latin scripts.