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New multi-word tokenizer Havon v4 outperforms GPT-4o on 204 languages

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new multi-word tokenizer called Havon v4, which demonstrates improved efficiency over GPT-4o's tokenizer across 204 languages. Havon v4, particularly when trained with a larger vocabulary, requires fewer tokens for many languages, including significant gains for English and Hindi. While Havon v3 showed strong performance on Indian languages, v4 builds upon it with a multi-word training stage, offering better compression for English and a broader range of languages, though scripts without spaces see less benefit. AI

IMPACT Offers potential for more efficient and cost-effective processing of multilingual text in LLMs.

RANK_REASON The cluster contains an academic paper detailing a new tokenizer model. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New multi-word tokenizer Havon v4 outperforms GPT-4o on 204 languages

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The cluster contains an academic paper detailing a new tokenizer model. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Johin Johny, Ankur Ghosh, Shervin Sam ·

    A Multi-Word Tokenizer for 204 Languages That Needs Fewer Tokens Than GPT-4o's, Including on English

    arXiv:2611.13059v1 Announce Type: cross Abstract: Languages of India need 1.6 to 14 times as many tokens as English on GPT-4o's tokenizer for the same content, which makes them more expensive and slower to serve. We train tokenizers from scratch on human-written text in about 200…

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