English(EN) Fewer Words, Not Fewer Tokens: Measuring the Sanskrit Tokenization Penalty per Proposition

研究发现：梵语分词惩罚高于英语

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-14 04:00

一项新的研究论文调查了现代语言模型处理梵语与英语和印地语相比的分词效率。研究发现，与英语相比，梵语每单位含义所需的Token数量明显更多，尤其是在使用具有大型词汇表的已部署分词器时。然而，与印地语相比，梵语的分词惩罚有所降低，并且随着词汇量的增大，差距进一步缩小。研究表明，虽然梵语的每词信息密度很高，但其复杂的形态学在实际应用中会导致每条命题的Token数量更高。 AI

影响突出了当前大型语言模型分词方法在处理梵语等信息密度高语言时可能存在的效率低下问题。

排序理由分析梵语分词效率的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.CL TIER_1 English(EN) · Devansh Sharma · 2026-09-14 04:00

字数更少，而非标记数更少：衡量每个命题的梵文标记化惩罚

arXiv:2609.12960v1 Announce Type: new Abstract: Sanskrit fuses case, number, person and tense into word endings and chains clauses into compounds, so it is information-dense per word. Whether that density survives subword tokenization is a separate question, to be asked per unit …

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字数更少，而非标记数更少：衡量每个命题的梵文标记化惩罚

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