mPLUG-DocOwl2: High-resolution Compressing for OCR-free Multi-page
  Document Understanding
Authors: name: Anwen Hu, name: Haiyang Xu, name: Liang Zhang, name: Jiabo Ye, name: Ming Yan, name: Ji Zhang, name: Qin Jin, name: Fei Huang, name: Jingren Zhou

Multimodel Large Language Models(MLLMs) have achieved promising OCR-free
Document Understanding performance by increasing the supported resolution of
document images. However, this comes at the cost of generating thousands of
visual tokens for a single document image, leading to excessive GPU memory and
slower inference times, particularly in multi-page document comprehension. In
this work, to address these challenges, we propose a High-resolution
DocCompressor module to compress each high-resolution document image into 324
tokens, guided by low-resolution global visual features. With this compression
module, to strengthen multi-page document comprehension ability and balance
both token efficiency and question-answering performance, we develop the
DocOwl2 under a three-stage training framework: Single-image Pretraining,
Multi-image Continue-pretraining, and Multi-task Finetuning. DocOwl2 sets a new
state-of-the-art across multi-page document understanding benchmarks and
reduces first token latency by more than 50%, demonstrating advanced
capabilities in multi-page questioning answering, explanation with evidence
pages, and cross-page structure understanding. Additionally, compared to
single-image MLLMs trained on similar data, our DocOwl2 achieves comparable
single-page understanding performance with less than 20% of the visual tokens.
Our codes, models, and data are publicly available at
https://github.com/X-PLUG/mPLUG-DocOwl/tree/main/DocOwl2.