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arXiv · 2508.13058

Do\u{g}al Dil \.I\c{s}lemede Tokenizasyon Standartlar{\i} ve \"Ol\c{c}\"um\"u: T\"urk\c{c}e \"Uzerinden B\"uy\"uk Dil Modellerinin Kar\c{s}{\i}la\c{s}t{\i}rmal{\i} Analizi

M. Ali Bayram·Ali Arda Fincan·Ahmet Semih Gümüş·Sercan Karakaş·Banu Diri·Savaş Yıldırım
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Abstract

Tokenization is a fundamental preprocessing step in Natural Language Processing (NLP), significantly impacting the capability of large language models (LLMs) to capture linguistic and semantic nuances. This study introduces a novel evaluation framework addressing tokenization challenges specific to morphologically-rich and low-resource languages such as Turkish. Utilizing the Turkish MMLU (TR-MMLU) dataset, comprising 6,200 multiple-choice questions from the Turkish education system, we assessed tokenizers based on vocabulary size, token count, processing time, language-specific token percentages (\%TR), and token purity (\%Pure). These newly proposed metrics measure how effectively tokenizers preserve linguistic structures. Our analysis reveals that language-specific token percentages exhibit a stronger correlation with downstream performance (e.g., MMLU scores) than token purity. Furthermore, increasing model parameters alone does not necessarily enhance linguistic performance, underscoring the importance of tailored, language-specific tokenization methods. The proposed framework establishes robust and practical tokenization standards for morphologically complex languages.

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M. Ali Bayram, Ali Arda Fincan, Ahmet Semih Gümüş, Sercan Karakaş, Banu Diri, Savaş Yıldırım. 2025-08-18. Do\u{g}al Dil \.I\c{s}lemede Tokenizasyon Standartlar{\i} ve \"Ol\c{c}\"um\"u: T\"urk\c{c}e \"Uzerinden B\"uy\"uk Dil Modellerinin Kar\c{s}{\i}la\c{s}t{\i}rmal{\i} Analizi. https://doi.org/10.1109/siu66497.2025.11112220

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