LangMAP adapts UnigramLM for multilingual use to deliver language-specific tokenization from a shared vocabulary, boosting boundary alignment metrics across natural and programming languages with mixed downstream fine-tuning gains.
Tokenization and the Noiseless Channel
6 Pith papers cite this work, alongside 10 external citations. Polarity classification is still indexing.
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Pith papers citing it
10
external citations · OpenAlex
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cs.CL 6years
2026 6representative citing papers
ConvexTok uses convex relaxation of tokenization to a linear program, improving intrinsic metrics, bits-per-byte, and some downstream tasks while certifying near-optimality within 1% at typical vocabulary sizes.
ToaST uses vocabulary-independent split trees and integer programming to produce tokenizers with over 11% fewer tokens than BPE, WordPiece, and UnigramLM while improving 1.5B-parameter LM scores on CORE.
On chemistry SMILES with a fixed 165-token base, BPE and Unigram-LM produce near-disjoint vocabularies (Jaccard ≤0.161) and Unigram-LM emits 29–41% more tokens across 22 matched conditions.
citing papers explorer
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LangMAP: A Language-Adaptive Approach to Tokenization
LangMAP adapts UnigramLM for multilingual use to deliver language-specific tokenization from a shared vocabulary, boosting boundary alignment metrics across natural and programming languages with mixed downstream fine-tuning gains.
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Tokenisation via Convex Relaxations
ConvexTok uses convex relaxation of tokenization to a linear program, improving intrinsic metrics, bits-per-byte, and some downstream tasks while certifying near-optimality within 1% at typical vocabulary sizes.
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Tokenization with Split Trees
ToaST uses vocabulary-independent split trees and integer programming to produce tokenizers with over 11% fewer tokens than BPE, WordPiece, and UnigramLM while improving 1.5B-parameter LM scores on CORE.
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Where to cut, how deep: BPE and Unigram-LM on chemistry SMILES
On chemistry SMILES with a fixed 165-token base, BPE and Unigram-LM produce near-disjoint vocabularies (Jaccard ≤0.161) and Unigram-LM emits 29–41% more tokens across 22 matched conditions.
- MinGram: A Minimalist Unigram Tokenizer with High Compression and Competitive Morphological Alignment
- Faster Superword Tokenization