BnMMLU

BnMMLU is a Bengali multiple-choice benchmark for measuring multitask language understanding across 41 subjects.

This repository exposes two dataset configs:

• full: the complete benchmark with 134,382 questions.
• hard: a 15,074-question subset containing the most challenging items, including a failure_ratio field.

The source data in this workspace is stored as CSV. For the Hugging Face dataset repo, the upload script converts the files to Parquet so that options is preserved as a real list instead of a stringified Python list.

Dataset Schema

full

Column	Type	Description
Unique_Serial	int64	Unique sequential identifier.
subject_name	string	Subject or domain label.
question	string	Bengali question text.
correct_answer	string	Correct option key: a, b, c, or d.
options	list[string]	Four answer options in order.
question_char_count	int64	Character count of the question text.

hard

The hard config contains all columns from full plus:

Column	Type	Description
failure_ratio	float64	Proportion of evaluated models that answered incorrectly.

Loading

from datasets import load_dataset

full = load_dataset("samanjoy2/BnMMLU", "full")
hard = load_dataset("samanjoy2/BnMMLU", "hard")

Citation

@inproceedings{joy-shatabda-2026-bnmmlu,
    title = "{B}n{MMLU}: Measuring Massive Multitask Language Understanding in {B}engali",
    author = "Joy, Saman Sarker  and
      Shatabda, Swakkhar",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2026.findings-acl.593/",
    doi = "10.18653/v1/2026.findings-acl.593",
    pages = "12211--12230",
    ISBN = "979-8-89176-395-1",
    abstract = "Large-scale multitask benchmarks have driven rapid progress in language modeling, yet most emphasize high-resource languages such as English, leaving Bengali underrepresented. We present BnMMLU, a comprehensive benchmark for measuring massive multitask language understanding in Bengali. BnMMLU spans 41 domains across STEM, humanities, social sciences, and general knowledge, and contains 134,375 multiple-choice question{--}option pairs-the most extensive Bengali evaluation suite to date. The dataset preserves mathematical content via MathML, and includes BnMMLU-HARD, a compact subset constructed from questions most frequently missed by top systems to stress difficult cases. We benchmark 24 model variants across 11 LLM families, spanning open-weights general/multilingual, Bengali-centric open-weights, and proprietary models, covering multiple parameter scales and instruction-tuned settings. We evaluate models under standardized protocols covering two prompting styles (Direct vs. Chain-of-Thought) and two context regimes (0-shot vs. 5-shot), reporting accuracy consistently across families. Our analysis highlights persistent gaps in reasoning and application skills and indicates sublinear returns to scale across model sizes. We release the dataset and evaluation templates to support rigorous, reproducible assessment of Bengali language understanding and to catalyze progress in multilingual NLP."
}