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LLM tokenization costs skyrocket for Bengali and Hindi, study finds

By PulseAugur Editorial · [1 sources] ·

A recent analysis reveals significant disparities in how large language models tokenize different languages, leading to increased computational costs for non-English text. While English text typically requires around 44 tokens for a given paragraph, Bengali can demand over 278 tokens on models like Llama 3.1, a 6.3x increase. This phenomenon is attributed to tokenizer design choices, with some models, like Gemma 3, demonstrating much lower tokenization costs for languages such as Bengali and Hindi due to larger, more inclusive vocabularies. AI

IMPACT Highlights potential cost and efficiency barriers for non-English language support in LLMs, prompting a need for more inclusive tokenizers.

RANK_REASON Analysis of LLM tokenization costs for different languages based on a research paper. [lever_c_demoted from research: ic=1 ai=1.0]

Read on dev.to — LLM tag →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

LLM tokenization costs skyrocket for Bengali and Hindi, study finds

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Analysis of LLM tokenization costs for different languages based on a research paper. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. dev.to — LLM tag TIER_1 English(EN) · frank chu ·

    Bengali costs 6.3x the tokens English does — I counted

    <h1> Bengali costs 6.3x the tokens English does — I counted </h1> <p>The same paragraph costs 278 tokens in Bengali and 44 in English on Llama 3.1's tokenizer — a 6.3x markup for saying the same thing. On Gemma 3, the same paragraph costs 51 tokens in Bengali and 44 in English, a…

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