KazMMLU: Evaluating Language Models on Kazakh, Russian, and Regional Knowledge of Kazakhstan

TL;DR

KazMMLU introduces a new dataset for evaluating language models on Kazakh and regional knowledge, revealing significant performance gaps and the need for specialized Kazakh-centric models.

Contribution

This paper presents KazMMLU, the first MMLU-style benchmark for Kazakh, including 23,000 questions across educational levels, to evaluate and improve multilingual models for Kazakh language.

Findings

State-of-the-art models perform poorly on Kazakh and Russian questions.

Large performance gaps exist compared to high-resource languages.

The dataset enables future research in Kazakh language modeling.

Abstract

Despite having a population of twenty million, Kazakhstan's culture and language remain underrepresented in the field of natural language processing. Although large language models (LLMs) continue to advance worldwide, progress in Kazakh language has been limited, as seen in the scarcity of dedicated models and benchmark evaluations. To address this gap, we introduce KazMMLU, the first MMLU-style dataset specifically designed for Kazakh language. KazMMLU comprises 23,000 questions that cover various educational levels, including STEM, humanities, and social sciences, sourced from authentic educational materials and manually validated by native speakers and educators. The dataset includes 10,969 Kazakh questions and 12,031 Russian questions, reflecting Kazakhstan's bilingual education system and rich local context. Our evaluation of several state-of-the-art multilingual models (Llama-3.1, Qwen-2.5, GPT-4, and DeepSeek V3) demonstrates substantial room for improvement, as even the best-performing models struggle to achieve competitive performance in Kazakh and Russian. These findings underscore significant performance gaps compared to high-resource languages. We hope that our dataset will enable further research and development of Kazakh-centric LLMs. Data and code will be made available upon acceptance.