SinhalaMMLU

For Research Purposes Only This dataset is intended solely for evaluating language models in academic and non-commercial research.

Overview

SinhalaMMLU is the first multiple-choice question answering benchmark designed specifically for Sinhala, a low-resource language.

• 7,000+ questions spanning secondary to collegiate education levels
• Aligned with the Sri Lankan national curriculum
• Covers 6 domains and 30 subjects, including both general academic topics and culturally grounded knowledge

We evaluated 26 large language models (LLMs) on SinhalaMMLU. Claude 3.5 Sonnet and GPT-4o achieved the highest average accuracies (67% and 62%, respectively), but overall model performance remains limited. Models particularly struggle in culturally rich domains such as the Humanities, highlighting significant room for improvement in adapting LLMs to low-resource and culturally specific contexts.

Dataset Details

The dataset is organized into three difficulty levels: Easy, Medium, and Hard. All levels share the same set of categories.

For access to the complete dataset resources, please contact pussewala.ashmari.ow4@naist.ac.jp.

For full details, see our paper: SinhalaMMLU: A Comprehensive Benchmark for Evaluating Multitask Language Understanding in Sinhala (EMNLP 2025)

License

This dataset is released under the Creative Commons Attribution–NonCommercial–NoDerivatives 4.0 International (CC BY-NC-ND 4.0) license.

SinhalaMMLU 2.0

For the ICTer SinhalaMMLU Shared Task, we will use hard-category test set with four answer options, created by randomly dropping one incorrect option from each five-option hard-category question reported in the paper. This test set will be used to evaluate models participating in the shared task.

Citation

If you use SinhalaMMLU in your research, please cite:

@inproceedings{pramodya-etal-2025-sinhalammlu,
    title     = "{S}inhala{MMLU}: A Comprehensive Benchmark for Evaluating Multitask Language Understanding in {S}inhala",
    author    = "Pramodya, Ashmari and Nelki, Nirasha and Shalinda, Heshan and Liyanage, Chamila and Sakai, Yusuke and
                 Pushpananda, Randil and Weerasinghe, Ruvan and Kamigaito, Hidetaka and Watanabe, Taro",
    editor    = "Christodoulopoulos, Christos and Chakraborty, Tanmoy and Rose, Carolyn and Peng, Violet",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
    month     = nov,
    year      = "2025",
    address   = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url       = "https://aclanthology.org/2025.emnlp-main.1673/",
    pages     = "32931--32949",
    ISBN      = "979-8-89176-332-6"
}

Contact

📧 pussewala.ashmari.ow4@naist.ac.jp