Reasoning about images with rich text, such as charts and documents, is a critical application of vision-language models (VLMs). However, VLMs often struggle in these domains due to the scarcity of diverse text-rich vision-language data. To address this challenge, we present CoSyn, a framework that leverages the coding capabilities of text-only large language models (LLMs) to automatically create synthetic text-rich multimodal data. Given input text describing a target domain (e.g., "nutrition fact labels"), CoSyn prompts an LLM to generate code (Python, HTML, LaTeX, etc.) for rendering synthetic images. With the underlying code as textual representations of the synthetic images, CoSyn can generate high-quality instruction-tuning data, again relying on a text-only LLM. Using CoSyn, we constructed a dataset comprising 400K images and 2.7M rows of vision-language instruction-tuning data. Comprehensive experiments on seven benchmarks demonstrate that models trained on our synthetic data achieve state-of-the-art performance among competitive open-source models, including Llama 3.2, and surpass proprietary models such as GPT-4V and Gemini 1.5 Flash. Furthermore, CoSyn can produce synthetic pointing data, enabling VLMs to ground information within input images, showcasing its potential for developing multimodal agents capable of acting in real-world environments.
View on arXiv@article{yang2025_2502.14846,
title={ Scaling Text-Rich Image Understanding via Code-Guided Synthetic Multimodal Data Generation },
author={ Yue Yang and Ajay Patel and Matt Deitke and Tanmay Gupta and Luca Weihs and Andrew Head and Mark Yatskar and Chris Callison-Burch and Ranjay Krishna and Aniruddha Kembhavi and Christopher Clark },
journal={arXiv preprint arXiv:2502.14846},
year={ 2025 }
}