AI’s ‘Tokenizer Tax’ Can Make Non-English Text More Expensive
Russian text used about 42% more tokens in one test, while a wider study found a 17x gap between languages. Results depend on the tokenizer.
BLEU, or Bilingual Evaluation Understudy, is an automatic metric originally developed for machine translation. It compares short sequences of words in generated text with one or more reference translations, combines precision across several n-gram lengths, and applies a penalty when output is too short. BLEU is inexpensive and useful for comparing systems on the same dataset, but it does not directly measure factuality, fluency, meaning, or human preference. Valid alternative wording can receive a low score, while awkward text can match many reference phrases. Results depend on tokenization and implementation, so reporting should include the exact evaluation settings and, when possible, human judgment.
Russian text used about 42% more tokens in one test, while a wider study found a 17x gap between languages. Results depend on the tokenizer.
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