Afri-MCQA: Multimodal Cultural Question Answering for African Languages

Paper

Overview

Afri-MCQA is the first multilingual cultural question-answering benchmark covering 8k Q&A pairs across 16 African languages from 13 countries. The benchmark offers parallel English-African language Q&A pairs across text and speech modalities, entirely created by native speakers.

Supported Tasks

• Visual Question Answering (VQA): Multiple-choice and open-ended QA grounded in culturally relevant images
• Visual Audio Question Answering: Speech-based QA grounded in culturally relevant images in native African languages and African-accented English
• Language Identification (LID): Identifying which of the 15 languages is spoken
• Automatic Speech Recognition (ASR): Transcribing spoken African language audio

Languages

Language - Country	Language Family	Region
Akan/Twi - Ghana	Niger-Congo / Volta-Niger	West
Amharic - Ethiopia	Afro-Asiatic / Ethio-Semitic	East
Chichewa - Malawi	Niger-Congo / Bantu	South & East
Hausa - Nigeria	Afro-Asiatic / Chadic	West
Igbo - Nigeria	Niger-Congo / Volta-Niger	West
Kikuyu - Kenya	Niger-Congo / Bantu	East
Kinyarwanda - Rwanda	Niger-Congo / Bantu	East
Lingala - DRC	Niger–Congo/ Bantu	Central Africa
Luganda - Uganda	Niger-Congo / Bantu	East
Oromo - Ethiopia	Afro-Asiatic / Cushitic	East
Setswana - Botswana	Niger-Congo / Bantu	South
Somali - Somalia	Afro-Asiatic / Cushitic	East
Tigrinya - Eritrea	Afro-Asiatic / Ethio-Semitic	East
Yoruba - Nigeria	Niger-Congo / Volta-Niger	West
Sesotho - Lesotho	Niger-Congo / Bantu	South
Zulu - South Africa	Niger-Congo / Bantu	South

Total speaker population: ~412.6 million

Dataset Structure

Each sample includes:

• image: Culturally relevant image
• question_english / question_native: Question in both languages
• options_english / options_native: Four multiple-choice options
• answer: Correct answer
• audio_question_english / audio_question_native: Audio recordings
• category: One of 10 cultural categories
• country / language: Origin metadata

Cultural Categories

🏛️ Geography & Landmarks | 👤 Public Figures & Pop Culture | 🍲 Cooking & Food | 👕 Objects & Clothing |🎭 Traditions & History | 🏢 Brands & Companies | 🌿 Plants & Animals | 👨‍👩‍👧 People & Everyday Life | 🚗 Vehicles & Transportation | ⚽ Sports & Recreation

Licensing

This dataset is released under CC-BY-NC-4.0.

Usage

from datasets import load_dataset

# Per language
amharic_dev = load_dataset("Atnafu/Afri-MCQA", "Amharic_dev", split="dev")
amharic_test = load_dataset("Atnafu/Afri-MCQA", "Amharic_test", split="test")

# All languages grouped
all_dev = load_dataset("Atnafu/Afri-MCQA", "all_dev", split="dev")
all_test = load_dataset("Atnafu/Afri-MCQA", "all_test", split="test")

Citation

@article{tonja2026afri,
  title={Afri-MCQA: Multimodal Cultural Question Answering for African Languages},
  author={Tonja, Atnafu Lambebo and Anand, Srija and Villa-Cueva, Emilio and Azime, Israel Abebe and Alabi, Jesujoba Oluwadara and Mohamed, Muhidin A and Yadeta, Debela Desalegn and Abadi, Negasi Haile and Oppong, Abigail and Obiefuna, Nnaemeka Casmir and others},
  journal={arXiv preprint arXiv:2601.05699},
  year={2026}
}