Scaling Text-Rich Image Understanding via Code-Guided Synthetic
  Multimodal Data Generation
Authors: name: Yue Yang, name: Ajay Patel, name: Matt Deitke, name: Tanmay Gupta, name: Luca Weihs, name: Andrew Head, name: Mark Yatskar, name: Chris Callison-Burch, name: Ranjay Krishna, name: Aniruddha Kembhavi, name: Christopher Clark

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.