Unverified50% confidenceBenchmarkExact time
The paper compares LCT with BPE, Unigram, and parity-aware BPE under the setting of 104 languages and a 200K vocabulary.
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50%
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Long-term
Relevance
10/13/2026
First Seen
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UnverifiedToken由BPE（字节对编码）算法生成，英文文本平均每个token约对应4个字符（约0.75个单词）65% similarVerifiedBPE的核心思路是从字符级别出发，反复合并出现频率最高的相邻字符对，直到词汇表达到预设大小64% similarVerifiedModern LLMs generally use BPE (Byte Pair Encoding) or WordPiece algorithms for tokenization.61% similarUnverified现代 LLM 通常使用字节对编码（BPE）或类似算法进行分词，英文中平均约 4 个字符对应 1 个 Token61% similarVerifiedBPE（Byte Pair Encoding）是大语言模型使用的Token化算法，通过统计训练语料中字符对的共现频率迭代合并最常见的字符组合61% similar
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