Afri-MCQA：非洲语言多模态文化问答数据集

摘要

非洲拥有全球超过三分之一的语言，但在人工智能研究领域却长期处于代表性不足的状态。我们推出Afri-MCQA——首个覆盖12个国家15种非洲语言、包含7.5万个问答对的多文化问答基准数据集。该基准提供跨文本与语音模态的平行英非双语问答对，所有内容均由母语者创建。基于Afri-MCQA对大语言模型的测试表明：开源模型在不同文化场景中表现欠佳，当使用本土语言或语音进行开放式视觉问答时，准确率趋近于零。为评估语言能力，我们设置了旨在区分文化知识与语言能力的对照实验，观察到模型在文本和语音模态下，本土语言与英语表现存在显著差距。这些发现凸显了发展语音优先技术、文化本位预训练及跨语言文化迁移的必要性。为支持非洲语言多模态AI的包容性发展，我们将Afri-MCQA数据集以学术许可或CC BY-NC 4.0协议发布于HuggingFace平台(https://huggingface.co/datasets/Atnafu/Afri-MCQA)。

English

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：非洲语言多模态文化问答数据集

Afri-MCQA: Multimodal Cultural Question Answering for African Languages

摘要

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