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LyngualLabs/Nigerian-Language-Exams-VQA

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The declared licence (cc-by-nc-4.0) is classified as having use restrictions.

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cc-by-nc-4.0, from the tagEvidence: README.md at 971f28a
Licence files
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Citation text from the card

If you use this dataset in your research, please cite:
```bibtex
@inproceedings{olufemi-etal-2025-challenging,
    title = "Challenging Multimodal {LLM}s with {A}frican Standardized Exams: A Document {VQA} Evaluation",
    author = "Olufemi, Victor Tolulope  and
      Babatunde, Oreoluwa Boluwatife  and
      Bolarinwa, Emmanuel  and
      Moshood, Kausar Yetunde",
    editor = "Lignos, Constantine  and
      Abdulmumin, Idris  and
      Adelani, David",
    booktitle = "Proceedings of the Sixth Workshop on African Natural Language Processing (AfricaNLP 2025)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2025.africanlp-1.22/",
    doi = "10.18653/v1/2025.africanlp-1.22",
    pages = "150--157",
    ISBN = "979-8-89176-257-2",
    abstract = "Despite rapid advancements in multimodal large language models (MLLMs), their ability to process low-resource African languages in document-based visual question answering (VQA) tasks remains limited. This paper evaluates three state-of-the-art MLLMs{---}GPT-4o, Claude-3.5 Haiku, and Gemini-1.5 Pro{---}on WAEC/NECO standardized exam questions in Yoruba, Igbo, and Hausa. We curate a dataset of multiple-choice questions from exam images and compare model accuracies across two prompting strategies: (1) using English prompts for African language questions, and (2) using native-language prompts. While GPT-4o achieves over 9

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dataset_licence({ "id": "LyngualLabs/Nigerian-Language-Exams-VQA" })
signal: "restrictive"
stated_licence: "cc-by-nc-4.0"
sources: 0