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arXiv · 2508.13044

B\"{u}y\"{u}k Dil Modelleri i\c{c}in TR-MMLU Benchmark{\i}: Performans De\u{g}erlendirmesi, Zorluklar ve \.{I}yile\c{s}tirme F{\i}rsatlar{\i}

M. Ali Bayram·Ali Arda Fincan·Ahmet Semih Gümüş·Banu Diri·Savaş Yıldırım·Öner Aytaş
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Abstract

Language models have made significant advancements in understanding and generating human language, achieving remarkable success in various applications. However, evaluating these models remains a challenge, particularly for resource-limited languages like Turkish. To address this issue, we introduce the Turkish MMLU (TR-MMLU) benchmark, a comprehensive evaluation framework designed to assess the linguistic and conceptual capabilities of large language models (LLMs) in Turkish. TR-MMLU is based on a meticulously curated dataset comprising 6,200 multiple-choice questions across 62 sections within the Turkish education system. This benchmark provides a standard framework for Turkish NLP research, enabling detailed analyses of LLMs' capabilities in processing Turkish text. In this study, we evaluated state-of-the-art LLMs on TR-MMLU, highlighting areas for improvement in model design. TR-MMLU sets a new standard for advancing Turkish NLP research and inspiring future innovations.

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M. Ali Bayram, Ali Arda Fincan, Ahmet Semih Gümüş, Banu Diri, Savaş Yıldırım, Öner Aytaş. 2025-08-18. B\"{u}y\"{u}k Dil Modelleri i\c{c}in TR-MMLU Benchmark{\i}: Performans De\u{g}erlendirmesi, Zorluklar ve \.{I}yile\c{s}tirme F{\i}rsatlar{\i}. https://doi.org/10.1109/siu66497.2025.11112154

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