SK-VQA: Synthetic Knowledge Generation at Scale for Training Context-Augmented Multimodal LLMs

• URL: http://arxiv.org/abs/2406.19593v2
• Date: Mon, 09 Jun 2025 21:57:56 GMT
• Title: SK-VQA: Synthetic Knowledge Generation at Scale for Training Context-Augmented Multimodal LLMs
• Authors: Xin Su, Man Luo, Kris W Pan, Tien Pei Chou, Vasudev Lal, Phillip Howard,
• Abstract summary: We introduce SK-VQA, a large-scale synthetic multimodal dataset containing over 2 million visual question-answer pairs.<n>Through human evaluations, we confirm the high quality of the generated question-answer pairs and their contextual relevance.<n>Our results indicate that models trained on SK-VQA demonstrate enhanced generalization in both context-aware VQA and multimodal RAG settings.
• Score: 6.879945062426145
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Multimodal retrieval augmented generation (RAG) plays a crucial role in domains such as knowledge-based visual question answering (KB-VQA), where external knowledge is needed to answer a question. However, existing multimodal LLMs (MLLMs) are not designed for context-augmented generation, limiting their effectiveness in such tasks. While synthetic data generation has recently gained attention for training MLLMs, its application for context-augmented generation remains underexplored. To address this gap, we introduce SK-VQA, a large-scale synthetic multimodal dataset containing over 2 million visual question-answer pairs, each associated with context documents containing information necessary to determine the final answer. Compared to previous datasets, SK-VQA contains 11x more unique questions, exhibits greater domain diversity, and covers a broader spectrum of image sources. Through human evaluations, we confirm the high quality of the generated question-answer pairs and their contextual relevance. Extensive experiments show that SK-VQA serves both as a challenging KB-VQA benchmark and as an effective training resource for adapting MLLMs to context-augmented generation. Our results further indicate that models trained on SK-VQA demonstrate enhanced generalization in both context-aware VQA and multimodal RAG settings. SK-VQA is publicly available via Hugging Face Hub.

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