Frequency-Ordered Tokenization for Better Text Compression
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arXiv
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866915819764580352 |
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| author | Kalcher, Maximilian |
| author_facet | Kalcher, Maximilian |
| contents | We present frequency-ordered tokenization, a simple preprocessing technique that improves lossless text compression by exploiting the power-law frequency distribution of natural language tokens (Zipf's law). The method tokenizes text with Byte Pair Encoding (BPE), reorders the vocabulary so that frequent tokens receive small integer identifiers, and encodes the result with variable-length integers before passing it to any standard compressor. On enwik8 (100 MB Wikipedia), this yields improvements of 7.08 percentage points (pp) for zlib, 1.69 pp for LZMA, and 0.76 pp for zstd (all including vocabulary overhead), outperforming the classical Word Replacing Transform. Gains are consistent at 1 GB scale (enwik9) and across Chinese and Arabic text. We further show that preprocessing accelerates compression for computationally expensive algorithms: the total wall-clock time including preprocessing is 3.1x faster than raw zstd-22 and 2.4x faster than raw LZMA, because the preprocessed input is substantially smaller. The method can be implemented in under 50 lines of code. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_22958 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Frequency-Ordered Tokenization for Better Text Compression Kalcher, Maximilian Information Theory Computation and Language 68P30 E.4; I.2.7 We present frequency-ordered tokenization, a simple preprocessing technique that improves lossless text compression by exploiting the power-law frequency distribution of natural language tokens (Zipf's law). The method tokenizes text with Byte Pair Encoding (BPE), reorders the vocabulary so that frequent tokens receive small integer identifiers, and encodes the result with variable-length integers before passing it to any standard compressor. On enwik8 (100 MB Wikipedia), this yields improvements of 7.08 percentage points (pp) for zlib, 1.69 pp for LZMA, and 0.76 pp for zstd (all including vocabulary overhead), outperforming the classical Word Replacing Transform. Gains are consistent at 1 GB scale (enwik9) and across Chinese and Arabic text. We further show that preprocessing accelerates compression for computationally expensive algorithms: the total wall-clock time including preprocessing is 3.1x faster than raw zstd-22 and 2.4x faster than raw LZMA, because the preprocessed input is substantially smaller. The method can be implemented in under 50 lines of code. |
| title | Frequency-Ordered Tokenization for Better Text Compression |
| topic | Information Theory Computation and Language 68P30 E.4; I.2.7 |
| url | https://arxiv.org/abs/2602.22958 |