Computer Science > Computation and Language
[Submitted on 28 Jun 2024 (v1), last revised 9 Jun 2025 (this version, v2)]
Title:SK-VQA: Synthetic Knowledge Generation at Scale for Training Context-Augmented Multimodal LLMs
View PDF HTML (experimental)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.
Submission history
From: Xin Su [view email][v1] Fri, 28 Jun 2024 01:14:43 UTC (3,337 KB)
[v2] Mon, 9 Jun 2025 21:57:56 UTC (2,325 KB)
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