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.
MorphBPE: A Morpho-Aware Tokenizer Bridging Linguistic Complexity for Efficient LLM Training Across Morphologies
2 Pith papers cite this work. Polarity classification is still indexing.
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cs.CL 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
A syllable-based tokenizer for Turkish enables a tiny 1.5M-parameter model to reach 50.3% Recall@5 on TQuAD retrieval, beating a much larger morphology baseline.
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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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HeceTokenizer: A Syllable-Based Tokenization Approach for Turkish Retrieval
A syllable-based tokenizer for Turkish enables a tiny 1.5M-parameter model to reach 50.3% Recall@5 on TQuAD retrieval, beating a much larger morphology baseline.