# AdaptBPE: From General Purpose to Specialized Tokenizers

arXiv: https://arxiv.org/abs/2601.21665 · HTML: https://mlsanity.com/paper/2601.21665/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 50 references that are in the map.
- First posted on arXiv 29 Jan 2026.

## Builds on (most cited first, 29 of 29)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Neural Machine Translation of Rare Words with Subword Units](https://mlsanity.com/paper/1508.07909/) (2015)
- [A Call for Clarity in Reporting BLEU Scores](https://mlsanity.com/paper/1804.08771/) (2018)
- [Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation](https://mlsanity.com/paper/1609.08144/) (2016)
- [Can You Trust Your Model's Uncertainty? Evaluating Predictive Uncertainty Under Dataset Shift](https://mlsanity.com/paper/1906.02530/) (2019)
- [Subword Regularization: Improving Neural Network Translation Models with Multiple Subword Candidates](https://mlsanity.com/paper/1804.10959/) (2018)
- [BPE-Dropout: Simple and Effective Subword Regularization](https://mlsanity.com/paper/1910.13267/) (2019)
- [Byte Pair Encoding is Suboptimal for Language Model Pretraining](https://mlsanity.com/paper/2004.03720/) (2020)
- [SIB-200: A Simple, Inclusive, and Big Evaluation Dataset for Topic Classification in 200+ Languages and Dialects](https://mlsanity.com/paper/2309.07445/) (2023)
- [Glot500: Scaling Multilingual Corpora and Language Models to 500 Languages](https://mlsanity.com/paper/2305.12182/) (2023)
- [WECHSEL: Effective initialization of subword embeddings for cross-lingual transfer of monolingual language models](https://mlsanity.com/paper/2112.06598/) (2021)
- [Unpacking Tokenization: Evaluating Text Compression and its Correlation with Model Performance](https://mlsanity.com/paper/2403.06265/) (2024)
- [Fast Vocabulary Transfer for Language Model Compression](https://mlsanity.com/paper/2402.09977/) (2024)
- [A Formal Perspective on Byte-Pair Encoding](https://mlsanity.com/paper/2306.16837/) (2023)
- [Trans-Tokenization and Cross-lingual Vocabulary Transfers: Language Adaptation of LLMs for Low-Resource NLP](https://mlsanity.com/paper/2408.04303/) (2024)
- [Efficient Domain Adaptation of Language Models via Adaptive Tokenization](https://mlsanity.com/paper/2109.07460/) (2021)
- [BPE Gets Picky: Efficient Vocabulary Refinement During Tokenizer Training](https://mlsanity.com/paper/2409.04599/) (2024)
- [Glitch Tokens in Large Language Models: Categorization Taxonomy and Effective Detection](https://mlsanity.com/paper/2404.09894/) (2024)
- [Task-Adaptive Tokenization: Enhancing Long-Form Text Generation Efficacy in Mental Health and Beyond](https://mlsanity.com/paper/2310.05317/) (2023)
- [An Efficient Multilingual Language Model Compression through Vocabulary Trimming](https://mlsanity.com/paper/2305.15020/) (2023)
- [Multi-word Tokenization for Sequence Compression](https://mlsanity.com/paper/2402.09949/) (2024)
- [Broken Tokens? Your Language Model can Secretly Handle Non-Canonical Tokenizations](https://mlsanity.com/paper/2506.19004/) (2025)
- [BPE vs. Morphological Segmentation: A Case Study on Machine Translation of Four Polysynthetic Languages](https://mlsanity.com/paper/2203.08954/) (2022)
- [From Characters to Words to in Between: Do We Capture Morphology?](https://mlsanity.com/paper/1704.08352/) (2017)
- [Effects of sub-word segmentation on performance of transformer language models](https://mlsanity.com/paper/2305.05480/) (2023)
- [Gold Panning in Vocabulary: An Adaptive Method for Vocabulary Expansion of Domain-Specific LLMs](https://mlsanity.com/paper/2410.01188/) (2024)
- [The Ups and Downs of Large Language Model Inference with Vocabulary Trimming by Language Heuristics](https://mlsanity.com/paper/2311.09709/) (2023)
- [Adapting Language Models via Token Translation](https://mlsanity.com/paper/2411.00593/) (2024)

## Built on by (most cited first, 3 of 3)

- [Emergent retokenization symmetry in large language models: phenomenology and applications](https://mlsanity.com/paper/2606.15521/) (2026)
- [Dynamic-Programming-Guided Hierarchical BPE and Empirical Analysis of Vocabulary Pruning](https://mlsanity.com/paper/2609.06898/) (2026)
- [To Each Language Its Tokenizer: Modular Tokenizers for Efficient Multilingual LLMs](https://mlsanity.com/paper/2609.15528/) (2026)

## Abstract (arXiv)

Subword tokenization methods, such as Byte-Pair Encoding (BPE), significantly impact the performance and efficiency of large language models (LLMs). The standard approach involves training a general-purpose tokenizer that uniformly processes all textual data during both training and inference. However, the use of a generic set of tokens can incur inefficiencies when applying the model to specific domains or languages. To address this limitation, we propose a post-training adaptation strategy that selectively replaces low-utility tokens with more relevant ones based on their frequency in an adaptation corpus. Our algorithm identifies the token inventory that most effectively encodes the adaptation corpus for a given target vocabulary size. Extensive experiments on generation and classification tasks across multiple languages demonstrate that our adapted tokenizers compress test corpora more effectively than baselines using the same vocabulary size. This method serves as a lightweight adaptation mechanism, akin to a vocabulary fine-tuning process, enabling optimized tokenization for specific domains or tasks. Our code and data are available at https://github.com/vijini/Adapt-BPE.git.