Research explores optimized tokenization for language models

A new research paper explores the impact of jointly optimizing tokenization with language modeling, comparing tokenizer-free approaches like SSLMs and H-Nets against fixed tokenizers. The study reveals that joint optimization fundamentally alters token structure, with SSLMs producing morphologically aligned and contextually efficient tokens, while H-Nets prioritize byte-level efficiency. These tokenizer-free methods consistently reduce language modeling perplexity and achieve competitive downstream performance, suggesting they optimize for contextual and computational efficiency. AI

IMPACT Optimized tokenization could lead to more efficient and performant language models across diverse languages.

RANK_REASON The cluster contains a research paper detailing novel methods for tokenization in language modeling. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

• arXiv
• Bert
• CatalyzeX
• DagsHub
• Gotit.pub
• H-Nets
• Hugging Face
• Saketh Reddy Vemula
• ScienceCast
• SSLMs

• paper
• model release

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