Afri-MCQA: Multimodal Cultural Question Answering for African Languages
Africa is home to over one-third of the world's languages, yet remains underrepresented in AI research. We introduce Afri-MCQA, the first Multilingual Cultural Question-Answering benchmark covering 7.5k Q&A pairs across 15 African languages from 12 countries. The benchmark offers parallel English-African language Q&A pairs across text and speech modalities and was entirely created by native speakers. Benchmarking large language models (LLMs) on Afri-MCQA shows that open-weight models perform poorly across evaluated cultures, with near-zero accuracy on open-ended VQA when queried in native language or speech. To evaluate linguistic competence, we include control experiments meant to assess this specific aspect separate from cultural knowledge, and we observe significant performance gaps between native languages and English for both text and speech. These findings underscore the need for speech-first approaches, culturally grounded pretraining, and cross-lingual cultural transfer. To support more inclusive multimodal AI development in African languages, we release our Afri-MCQA under academic license or CC BY-NC 4.0 on HuggingFace (https://huggingface.co/datasets/Atnafu/Afri-MCQA)
6964d050138cc47cbd76547f
Atnafu Lambebo Tonja
claimed_verified
2026-01-12T14:16:39.565Z
6245232cf59b8d262df30db6
https://cdn-avatars.huggingface.co/v1/production/uploads/6245232cf59b8d262df30db6/zmQuv1vqGL3MsCAnzGm9y.jpeg
Atnafu Lambebo Tonja
Atnafu
user
Atnafu
6964d050138cc47cbd765480
Srija Anand
6964d050138cc47cbd765481
Emilio Villa-Cueva
6964d050138cc47cbd765482
Israel Abebe Azime
claimed_verified
2026-04-07T08:49:30.400Z
603581672b01f31f03359c99
https://cdn-avatars.huggingface.co/v1/production/uploads/603581672b01f31f03359c99/UQdE-Z5Ua2yF2q6SaLa1z.jpeg
Israel Abebe Azime
israel
user
israel
6964d050138cc47cbd765483
Jesujoba Oluwadara Alabi
6964d050138cc47cbd765484
Muhidin A. Mohamed
6964d050138cc47cbd765485
Debela Desalegn Yadeta
6964d050138cc47cbd765486
Negasi Haile Abadi
6964d050138cc47cbd765487
Abigail Oppong
6964d050138cc47cbd765488
Nnaemeka Casmir Obiefuna
6964d050138cc47cbd765489
Idris Abdulmumin
6964d050138cc47cbd76548a
Naome A Etori
6964d050138cc47cbd76548b
Eric Peter Wairagala
6964d050138cc47cbd76548c
Kanda Patrick Tshinu
6964d050138cc47cbd76548d
Imanigirimbabazi Emmanuel
6964d050138cc47cbd76548e
Gabofetswe Malema
6964d050138cc47cbd76548f
Alham Fikri Aji
6964d050138cc47cbd765490
David Ifeoluwa Adelani
6964d050138cc47cbd765491
Thamar Solorio
Afri-MCQA: Multimodal Cultural Question Answering for African Languages
Africa is home to over one-third of the world's languages, yet remains underrepresented in AI research. We introduce Afri-MCQA, the first Multilingual Cultural Question-Answering benchmark covering 7.5k Q&A pairs across 15 African languages from 12 countries. The benchmark offers parallel English-African language Q&A pairs across text and speech modalities and was entirely created by native speakers. Benchmarking large language models (LLMs) on Afri-MCQA shows that open-weight models perform poorly across evaluated cultures, with near-zero accuracy on open-ended VQA when queried in native language or speech. To evaluate linguistic competence, we include control experiments meant to assess this specific aspect separate from cultural knowledge, and we observe significant performance gaps between native languages and English for both text and speech. These findings underscore the need for speech-first approaches, culturally grounded pretraining, and cross-lingual cultural transfer. To support more inclusive multimodal AI development in African languages, we release our Afri-MCQA under academic license or CC BY-NC 4.0 on HuggingFace (https://huggingface.co/datasets/Atnafu/Afri-MCQA)
Afri-MCQA benchmark demonstrates poor performance of open-weight LLMs in African languages, highlighting the need for culturally grounded pretraining and speech-first approaches in AI development.
Multilingual Cultural Question-Answering large language models cross-lingual cultural transfer speech-first approaches culturally grounded pretraining