BnMMLU: Measuring Massive Multitask Language Understanding in Bengali

ACL ARR 2025 February Submission1548 Authors

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.

Paper Type: Long

Research Area: Resources and Evaluation

Research Area Keywords: Datasets for low-resource languages, Benchmarking, Language resources, NLP datasets, Evaluation methodologies

Contribution Types: Approaches to low-resource settings, Data resources, Data analysis

Languages Studied: Bengali

Submission Number: 1548