Text-Only Data Synthesis for Vision Language Model Training

📰 ArXiv cs.AI

Learn to synthesize multimodal data from text-only inputs for vision language model training, reducing data collection costs

advanced Published 28 May 2026

Action Steps

Propose a cross-integrated three-stage multimodal data synthesis framework to generate synthetic datasets
Generate datasets like Unicorn-1.2M using the proposed framework
Evaluate the quality of the synthesized datasets for vision language model training
Compare the performance of models trained on synthetic datasets versus traditional image-text pairs
Apply the synthesized datasets to train vision-language models and assess their performance

Who Needs to Know This

Machine learning engineers and researchers working on vision-language models can benefit from this technique to generate high-quality training data without the need for large-scale image-text pairs

Key Insight

💡 High-quality multimodal training data can be synthesized purely from text, reducing data collection costs

Full Article

Title: Text-Only Data Synthesis for Vision Language Model Training

Abstract:
arXiv:2503.22655v2 Announce Type: replace Abstract: Training vision-language models (VLMs) typically requires large-scale, high-quality image-text pairs, but collecting or synthesizing such data is costly. In contrast, text data is abundant and inexpensive, prompting the question: can high-quality multimodal training data be synthesized purely from text? To tackle this, we propose a cross-integrated three-stage multimodal data synthesis framework, which generates two datasets: Unicorn-1.2M and U

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