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/",
    pages = "12211--12230",
    ISBN = "979-8-89176-395-1"
}