mPLUG-DocOwl: Modularized Multimodal Large Language Model for Document
  Understanding
Jiabo Ye Anwen Hu Haiyang Xu Qinghao Ye Ming Yan Yuhao Dan Chenlin Zhao Guohai Xu Chenliang Li Junfeng Tian Qian Qi Ji Zhang Fei Huang
Document understanding refers to automatically extract, analyze and
comprehend information from various types of digital documents, such as a web
page. Existing Multi-model Large Language Models (MLLMs), including mPLUG-Owl,
have demonstrated promising zero-shot capabilities in shallow OCR-free text
recognition, indicating their potential for OCR-free document understanding.
Nevertheless, without in-domain training, these models tend to ignore
fine-grained OCR features, such as sophisticated tables or large blocks of
text, which are essential for OCR-free document understanding. In this paper,
we propose mPLUG-DocOwl based on mPLUG-Owl for OCR-free document understanding.
Specifically, we first construct a instruction tuning dataset featuring a wide
range of visual-text understanding tasks. Then, we strengthen the OCR-free
document understanding ability by jointly train the model on language-only,
general vision-and-language, and document instruction tuning dataset with our
unified instruction tuning strategy. We also build an OCR-free document
instruction understanding evaluation set LLMDoc to better compare models'
capabilities on instruct compliance and document understanding. Experimental
results show that our model outperforms existing multi-modal models,
demonstrating its strong ability of document understanding. Besides, without
specific fine-tuning, mPLUG-DocOwl generalizes well on various downstream
tasks. Our code, models, training data and evaluation set are available at
https://github.com/X-PLUG/mPLUG-DocOwl.