EverydayMMQA: A Multilingual and Multimodal Framework for Culturally Grounded Spoken Visual QA

• URL: http://arxiv.org/abs/2510.06371v1
• Date: Tue, 07 Oct 2025 18:37:32 GMT
• Title: EverydayMMQA: A Multilingual and Multimodal Framework for Culturally Grounded Spoken Visual QA
• Authors: Firoj Alam, Ali Ezzat Shahroor, Md. Arid Hasan, Zien Sheikh Ali, Hunzalah Hassan Bhatti, Mohamed Bayan Kmainasi, Shammur Absar Chowdhury, Basel Mousi, Fahim Dalvi, Nadir Durrani, Natasa Milic-Frayling,
• Abstract summary: We introduce Everyday Multimodal and Multilingual QA (EverydayMMQA), a framework for creating large-scale, culturally-grounded datasets for spoken and visual question answering (SVQA)<n>OASIS is a multimodal dataset integrating speech, images, and text.<n>We benchmarked four closed-source models, three open-source models, and one fine-tuned model.
• Score: 22.30611382189773
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Large-scale multimodal models achieve strong results on tasks like Visual Question Answering (VQA), but they often fail when queries require culturally grounded, everyday knowledge, particularly in low-resource and underrepresented languages. To bridge this gap, we introduce Everyday Multimodal and Multilingual QA (EverydayMMQA), a framework for creating large-scale, culturally-grounded datasets for spoken and visual question answering (SVQA). Using this framework, we developed OASIS, a multimodal dataset integrating speech, images, and text. With over ~0.92M images and 14.8M QA pairs, OASIS contains 3.7M spoken questions, enabling four unique input combinations: speech-only, text-only, speech+image, and text+image. Focused on English and Arabic varieties, 18 countries, the dataset content is curated to reflect diverse, real-world situations. OASIS tests models on tasks beyond object recognition that involve pragmatic, commonsense, and culturally aware reasoning. We benchmarked four closed-source models, three open-source models, and one fine-tuned model. EverydayMMQA and OASIS together provide a benchmark and training dataset for building multimodal LLMs for a comprehensive set of everyday tasks within cultural contexts. The framework and dataset will be made publicly available to the community.

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