BnMMLU: Measuring Massive Multitask Language Understanding in Bengali

• URL: http://arxiv.org/abs/2505.18951v1
• Date: Sun, 25 May 2025 02:54:31 GMT
• Title: BnMMLU: Measuring Massive Multitask Language Understanding in Bengali
• Authors: Saman Sarker Joy,
• Abstract summary: We introduce BnMMLU, a benchmark to evaluate the language understanding capabilities of Bengali in language models.<n>The dataset spans 23 domains, including science, humanities, mathematics and general knowledge.<n>We benchmark several proprietary and open-source large language models (LLMs) on the BnMMLU test set.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The Massive Multitask Language Understanding (MMLU) benchmark has been widely used to evaluate language models across various domains. However, existing MMLU datasets primarily focus on high-resource languages such as English, which leaves low-resource languages like Bengali underrepresented. In this paper, we introduce BnMMLU, a benchmark to evaluate the multitask language understanding capabilities of Bengali in language models. The dataset spans 23 domains, including science, humanities, mathematics and general knowledge and is structured in a multiple-choice format to assess factual knowledge, application-based problem-solving and reasoning abilities of language models. It consists of 138,949 question-option pairs. We benchmark several proprietary and open-source large language models (LLMs) on the BnMMLU test set. Additionally, we annotate the test set with three cognitive categories-factual knowledge, procedural application and reasoning-to gain deeper insights into model strengths and weaknesses across various cognitive tasks. The results reveal significant performance gaps, highlighting the need for improved pre-training and fine-tuning strategies tailored to Bengali data. We release the dataset and benchmark results to facilitate further research in this area.

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