| Challenge: | Current language models use a static tokenizer, which results in degraded efficiency and language capabilities. |
| Approach: | They propose a way to dynamically decide on token boundaries based on input text . they merge frequent subword sequences in a batch and apply a hypernetwork to compute token embeddings . |
| Outcome: | The proposed method reduces token sequence lengths by >20% across 14 languages while promoting fairness across languages. |
Similar Papers
FLEXITOKENS: Flexible Tokenization for Evolving Language Models (2026.findings-acl)
Copied to clipboard
| Challenge: | Widely used subword tokenizers overfragment sequences in unseen domains, languages, and scripts . inefficient tokenizer models can cause overfragments in out-of-distribution domains if not trained properly . |
| Approach: | They propose a byte-level LM with learnable tokenizers to make tokenization adaptive . they propose 'flexitoken' which enables significantly greater flexibility during adaptation . |
| Outcome: | The proposed method significantly reduces token overfragmentation and improves on multilingual benchmarks and domains. |
Learn Your Tokens: Word-Pooled Tokenization for Language Modeling (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Language models typically tokenize text into subwords, using a deterministic, hand-engineered heuristic of combining characters into longer surface-level strings such as ‘ing’ or whole words. |
| Approach: | They propose a 'learn your tokens' scheme which pooles bytes/characters into word representations and decodes individual characters/bytes per word in parallel. |
| Outcome: | The proposed tokenizer outperforms subword models and byte/character models over the word boundary and outperformed on rare words by a factor of 30! |
AdaptBPE: From General Purpose to Specialized Tokenizers (2026.eacl-long)
Copied to clipboard
| Challenge: | Subword tokenization methods impact performance and efficiency of large language models . generic tokens can incur inefficiencies when applying the model to specific domains or languages . |
| Approach: | They propose a subword tokenization technique that selectively replaces low-utility tokens with more relevant ones based on their frequency in an adaptation corpus. |
| Outcome: | The proposed method compresses test corpora more effectively than baselines using the same vocabulary size. |
Tokenizer-Aware Cross-Lingual Adaptation of Decoder-Only LLMs through Embedding Relearning and Swapping (2026.eacl-long)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) have been primarily focused on English, leaving the multilingual ability unexplored. |
| Approach: | They propose a technique that creates new tokenizers and tunes embeddings on fixed model weights for target language adaptation. |
| Outcome: | The proposed method is light-weight and performant but has limitations for older models and high resource languages. |
Tokenization Falling Short: On Subword Robustness in Large Language Models (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Language models typically tokenize raw text into sequences of subword identifiers from a predefined vocabulary. |
| Approach: | They propose to tokenize raw text into sequences of subword identifiers from a predefined vocabulary . they also investigate the challenges and their impact on large language models . |
| Outcome: | The proposed model can mitigate tokenization issues, but still suffer from typos and other variations. |
BPE-knockout: Pruning Pre-existing BPE Tokenisers with Backwards-compatible Morphological Semi-supervision (2024.naacl-long)
Copied to clipboard
| Challenge: | Subword tokenisation is a mainstay in natural language processing (NLP) it allows the representation of an infinite space of text with a finite set of units. |
| Approach: | They propose to use byte-pair encoding to represent an infinite space of text with a finite set of units by removing subwords from the BPE vocabulary without impeding further use of merges that relied on them. |
| Outcome: | The proposed method improves token-based tasks in English, Dutch and German while saving training costs. |
From Characters to Tokens: Dynamic Grouping with Hierarchical BPE (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Subword tokenization methods suffer from inefficiencies in representing rare words and require large embedding matrices. |
| Approach: | They propose a dynamic character grouping method that leverages existing BPE tokenization without adding additional models. |
| Outcome: | The proposed method matches or exceeds existing entropy- and whitespace-based patching strategies while maintaining a compact vocabulary. |
Where are we Still Split on Tokenization? (2024.findings-eacl)
Copied to clipboard
| Challenge: | Identifying tokens is a crucial first step for many tasks in Natural Language Processing (NLP) gold tokenization is often assumed, but some work on token-level tasks is more challenging. |
| Approach: | They propose an efficient method for tokenization with subword-based language models and evaluate it on 122 languages in 20 scripts. |
| Outcome: | The proposed method performs on par with the state-of-the-art on 122 languages in 20 scripts. |
One Tokenizer To Rule Them All: Emergent Language Plasticity via Multilingual Tokenizers (2026.acl-long)
Copied to clipboard
Diana Abagyan, Alejandro R. Salamanca, Andres Felipe Cruz-Salinas, Kris Cao, Hangyu Lin, Acyr Locatelli, Marzieh Fadaee, Ahmet Üstün, Sara Hooker
| Challenge: | Existing approaches to train multilingual large language models for many languages at once are limited due to limited model capacity, scarce high-quality data, and compute constraints. |
| Approach: | They propose to use a universal tokenizer to improve language plasticity and adaptability to new languages by up to 20%. |
| Outcome: | The proposed tokenizer improves language plasticity and improves plasticity towards languages that are completely unseen in the tokenizer and pretraining, by up to 5% win rate gain. |
Stop Taking Tokenizers for Granted: They Are Core Design Decisions in Large Language Models (2026.eacl-long)
Copied to clipboard
| Challenge: | Subword tokenization approaches misalign with linguistic structure and waste capacity across languages and domains. |
| Approach: | They argue for a context-aware framework that integrates tokenizer and model co-design . they argue that tokenization should be treated as a core design problem, not an afterthought . |
| Outcome: | The proposed framework integrates tokenizer and model co-design, guided by linguistic, domain, and deployment considerations. |