Oasis: One Image is All You Need for Multimodal Instruction Data Synthesis

• URL: http://arxiv.org/abs/2503.08741v3
• Date: Wed, 26 Mar 2025 09:01:55 GMT
• Title: Oasis: One Image is All You Need for Multimodal Instruction Data Synthesis
• Authors: Letian Zhang, Quan Cui, Bingchen Zhao, Cheng Yang,
• Abstract summary: We propose a new method, Oasis, to synthesize high-quality multi-modal data with only images.<n>Oasis breaks through traditional methods by prompting only images to the MLLMs.<n>Our method features a delicate quality control method which ensures the data quality.
• Score: 19.75619888353222
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The success of multi-modal large language models (MLLMs) has been largely attributed to the large-scale training data. However, the training data of many MLLMs is unavailable due to privacy concerns. The expensive and labor-intensive process of collecting multi-modal data further exacerbates the problem. Is it possible to synthesize multi-modal training data automatically without compromising diversity and quality? In this paper, we propose a new method, Oasis, to synthesize high-quality multi-modal data with only images. Oasis breaks through traditional methods by prompting only images to the MLLMs, thus extending the data diversity by a large margin. Our method features a delicate quality control method which ensures the data quality. We collected over 500k data and conducted incremental experiments on LLaVA-NeXT. Extensive experiments demonstrate that our method can significantly improve the performance of MLLMs. The image-based synthesis also allows us to focus on the specific-domain ability of MLLMs. Code and dataset are publicly available at https://github.com/Letian2003/MM_INF.

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