@inproceedings{tonja-etal-2026-afri,
    title = "Afri-{MCQA}: Multimodal Cultural Question Answering for {A}frican 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
      Abdulmumin, Idris  and
      Etori, Naome A  and
      Wairagala, Eric Peter  and
      Tshinu, Kanda Patrick  and
      Emmanuel, Imanigirimbabazi  and
      Malema, Gabofetswe  and
      Aji, Alham Fikri  and
      Adelani, David Ifeoluwa  and
      Solorio, Thamar",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-long.1869/",
    pages = "40249--40282",
    ISBN = "979-8-89176-390-6",
    abstract = "Africa is home to over one-third of the world{'}s languages, yet remains severely underrepresented in multimodal AI research. We introduce Afri-MCQA, the first Multilingual Cultural Question-Answering benchmark containing 7.5k Q A pairs across 15 African languages from 12 countries. The benchmark offers parallel text and speech modalities and was entirely created by native speakers. We find that models show poor performance across evaluated cultures, with near-zero accuracy on open-ended VQA when queried through native language or speech. To test 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 pressing need for speech-first approaches, culturally grounded pretraining, and cross-lingual cultural transfer. We release Afri-MCQA to support more inclusive multimodal AI development."
}