Stop Taking Tokenizers for Granted: They Are Core Design Decisions in Large Language Models (2026.eacl-long)
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| 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. |
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Negar Foroutan, Clara Meister, Debjit Paul, Joel Niklaus, Sina Ahmadi, Antoine Bosselut, Rico Sennrich
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| Challenge: | Multilingual language models perform surprisingly well in a variety of NLP tasks for diverse languages. |
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| 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. |
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| Challenge: | Tokenization is a fundamental task in natural language processing that forms the first step of many pipelines. |
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Multilingual Tokenization through the Lens of Indian Languages: Challenges and Insights (2026.findings-acl)
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Maharaj Brahma, N J Karthika, Rajat Verma, Nagasai Saketh Naidu, Rohit Saluja, Maunendra Sankar Desarkar, Ganesh Ramakrishnan
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| Challenge: | Current vocabulary adaptation approaches append the target domainspecific vocabulary (V DOMAIN) at the end of the PLM vocabulary. |
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