SinhalaMMLU

We introduce SinhalaMMLU, the first multiple-choice question answering benchmark designed specifically for Sinhala, a low-resource language.
The dataset contains over 7,000 questions spanning secondary to collegiate education levels, aligned with the Sri Lankan national curriculum.
It covers six domains and 30 subjects, encompassing both general academic topics and culturally grounded knowledge. We evaluated 26 large language models (LLMs) on SinhalaMMLU and observed that, while Claude 3.5 Sonnet and GPT-4o achieved the highest average accuracies of 67% and 62% respectively, overall model performance remains limited. Notably, models 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

This dataset consists of three difficulty levels — Easy, Medium, and Hard — which share the same set of categories across the full dataset.
For access to the complete dataset resources, please contact the pussewala.ashmari.ow4@naist.ac.jp **.

More details on the dataset can be found in paper SinhalaMMLU: A Comprehensive Benchmark for Evaluating Multitask Language Understanding in Sinhala

License

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

Citation

@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