l3cube-pune/IndicQuest-v2
IndicQuest v2 A gold-standard multilingual question-answering benchmark for evaluating the India-specific factual knowledge of Large Language Models. 3,471 curriculum-grounded English question–answer pairs across nine domains, translated into 19 Indic languages: 69,420 parallel pairs across 20 languages. More details can be found in our paper. Dataset structure One CSV per language, named <language>.csv (english.csv, hindi.csv, marathi.csv, …). Every file has the… See the full description on the dataset page: https://huggingface.co/datasets/l3cube-pune/IndicQuest-v2.
IndicQuest v2
A gold-standard multilingual question-answering benchmark for evaluating the India-specific factual knowledge of Large Language Models. 3,471 curriculum-grounded English question–answer pairs across nine domains, translated into 19 Indic languages: 69,420 parallel pairs across 20 languages.
More details can be found in our paper.
Dataset structure
One CSV per language, named <language>.csv (english.csv, hindi.csv, marathi.csv, …). Every file has the same 3,471 rows in the same order, so the nth row of any file is the translation of the nth row of english.csv.
Domains
Languages
English plus Assamese, Bengali, Dogri, Gujarati, Hindi, Kannada, Konkani, Maithili, Malayalam, Marathi, Meitei (Manipuri), Nepali, Odia, Punjabi, Sanskrit, Sindhi, Tamil, Telugu, Urdu.
Example
Usage
from datasets import load_dataset
english = load_dataset("l3cube-pune/IndicQuest-v2", data_files="english.csv")
hindi = load_dataset("l3cube-pune/IndicQuest-v2", data_files="hindi.csv")Citing
@article{jain2026l3cube,
title={L3Cube-IndicQuest v2: A Large-Scale Multilingual Benchmark for Evaluating Factual Knowledge of Large Language Models Across Indic Languages},
author={Jain, Rinit and Mahajan, Tirthraj and Joshi, Advait and Joshi, Raviraj},
journal={arXiv preprint arXiv:2608.15535},
year={2026}
}