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English(EN) Tokenisation via Convex Relaxations

新的ConvexTok算法使用凸优化来优化NLP分词

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员开发了一种新的分词算法，称为ConvexTok，它使用凸优化来构建分词器。与现有的贪婪方法（如BPE和Unigram）不同，ConvexTok考虑整个词汇表以做出最优决策。该算法在分词指标、语言模型的每字节比特数方面表现出改进，并提供了最优性的认证，在常见的词汇量大小下，其结果接近最优值的1%。 AI

影响 引入了一种新颖、更优化的分词方法，可以提高语言模型的效率和性能。

排序理由 该集群包含一篇详细介绍NLP分词新算法的研究论文。

在 arXiv cs.CL 阅读 →

AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →

新的ConvexTok算法使用凸优化来优化NLP分词

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该集群包含一篇详细介绍NLP分词新算法的研究论文。
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报道来源 [2]

  1. arXiv cs.CL TIER_1 English(EN) · Jan Tempus, Philip Whittington, Craig W. Schmidt, Dennis Komm, Tiago Pimentel ·

    通过凸松弛进行分词

    arXiv:2605.22821v1 Announce Type: new Abstract: Tokenisation is an integral part of the current NLP pipeline. Current tokenisation algorithms such as BPE and Unigram are greedy algorithms -- they make locally optimal decisions without considering the resulting vocabulary as a who…

  2. arXiv cs.CL TIER_1 English(EN) · Tiago Pimentel ·

    通过凸松弛进行分词

    Tokenisation is an integral part of the current NLP pipeline. Current tokenisation algorithms such as BPE and Unigram are greedy algorithms -- they make locally optimal decisions without considering the resulting vocabulary as a whole. We instead formulate tokeniser construction …

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