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

新的多词分词器Havon v4在204种语言上表现优于GPT-4o

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了一种名为Havon v4的新型多词分词器，该分词器在204种语言上的效率均优于GPT-4o的分词器。Havon v4，尤其是在使用更大的词汇表进行训练时，对许多语言所需的词数更少，包括英语和印地语都有显著的提升。虽然Havon v3在印度语言上表现强劲，但v4在其基础上增加了多词训练阶段，为英语和更广泛的语言提供了更好的压缩效果，尽管没有空格的文字脚本获益较少。 AI

影响 为大型语言模型中多语言文本的更高效、更具成本效益的处理提供了潜力。

排序理由 该集群包含一篇详细介绍新型分词器模型的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

新的多词分词器Havon v4在204种语言上表现优于GPT-4o

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该集群包含一篇详细介绍新型分词器模型的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

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

    一种支持204种语言的多词分词器，其英文分词数量少于GPT-4o

    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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