LayoutLLM: Layout Instruction Tuning with Large Language Models for Document Understanding

Chuwei Luo, Yufan Shen, Zhaoqing Zhu, Qi Zheng, Zhi Yu, Cong Yao; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024, pp. 15630-15640

Recently leveraging large language models (LLMs) or multimodal large language models (MLLMs) for document understanding has been proven very promising. However previous works that employ LLMs/MLLMs for document understanding have not fully explored and utilized the document layout information which is vital for precise document understanding. In this paper we propose LayoutLLM an LLM/MLLM based method for document understanding. The core of LayoutLLM is a layout instruction tuning strategy which is specially designed to enhance the comprehension and utilization of document layouts. The proposed layout instruction tuning strategy consists of two components: Layout-aware Pre-training and Layout-aware Supervised Fine-tuning. To capture the characteristics of document layout in Layout-aware Pre-training three groups of pre-training tasks corresponding to document-level region-level and segment-level information are introduced. Furthermore a novel module called layout chain-of-thought (LayoutCoT) is devised to enable LayoutLLM to focus on regions relevant to the question and generate accurate answers. LayoutCoT is effective for boosting the performance of document understanding. Meanwhile it brings a certain degree of interpretability which could facilitate manual inspection and correction. Experiments on standard benchmarks show that the proposed LayoutLLM significantly outperforms existing methods that adopt open-source 7B LLMs/MLLMs for document understanding.