English(EN) Joint Optimization for Greedy Longest-match Tokenization

新的JOLT方法优化分词，优于BPE

作者 PulseAugur 编辑部 · [1 个来源] · 2026-07-28 04:00

研究人员开发了一种名为联合优化贪婪最长匹配分词（JOLT）的新方法，以改进子词词汇训练。JOLT将词汇学习表述为整数规划问题，确保优化后的分词与实践中使用的贪婪最长匹配解码一致，这与以往基于启发式的方法（如字节对编码（BPE））不同。该方法通过求解线性规划松弛并选择性地添加高阶分词来扩展。JOLT展示了显著的改进，缩小了BPE留下的89.6-99.4%的压缩差距，并在保留数据上产生的词元数量减少了多达0.78%。 AI

影响这项研究提供了一种更有效的分词方法，有望降低NLP模型的计算成本并提高其性能。

排序理由关于新的分词优化方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CL 阅读 →

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Newsworthiness bucket: Tool; 关于新的分词优化方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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arXiv cs.CL TIER_1 English(EN) · Adhiraj Singh, Deepanshu Mody, Ghina Al Shdaifat, Hamza Alshamy, Adam Wiemerslage, Varshini Reddy, Craig W. Schmidt · 2026-07-28 04:00

联合优化贪婪最长匹配分词

arXiv:2607.23362v1 Announce Type: new Abstract: Recent work has shown that subword vocabularies can be trained to optimize compression for a specific inference rule rather than relying on greedy heuristics such as Byte Pair Encoding (BPE). We extend this approach to greedy left-t…

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联合优化贪婪最长匹配分词

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