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

**Authors:** Chuwei Luo, Yufan Shen, Zhaoqing Zhu, Qi Zheng, Zhi Yu, Cong Yao  
**arXiv:** [2404.05225](https://arxiv.org/abs/2404.05225)  
**Date:** 2024-04-08  
**Summary page:** https://www.summarizepaper.com/en/paper/2404.05225v1/

## Key points

- Use of large language models (LLMs) and multimodal large language models (MLLMs) for document understanding has shown great potential
- Existing approaches utilizing LLMs/MLLMs have not fully utilized crucial information provided by document layouts
- Introduction of LayoutLLM method with a layout instruction tuning strategy to enhance comprehension and utilization of document layouts
- Layout-aware Pre-training and Layout-aware Supervised Fine-tuning components in the strategy focusing on capturing nuances of document layout at different levels
- Incorporation of a module to enable LayoutLLM to focus on relevant regions and generate precise answers based on queries
- Experimental results show significant improvements in document understanding using open-source 7B LLMs/MLLMs
- Training data for LayoutLLM is publicly available at https://github.com/AlibabaResearch/AdvancedLiterateMachinery/tree/main/DocumentUnderstanding/LayoutLLM
- Authors Chuwei Luo, Yufan Shen, Zhaoqing Zhu, Qi Zheng, Zhi Yu, and Cong Yao present their findings in the paper titled "LayoutLLM: Layout Instruction Tuning with Large Language Models for Document Understanding," submitted to CVPR 2024 conference

## Summary

In recent years, the use of large language models (LLMs) and multimodal large language models (MLLMs) for document understanding has shown great potential. However, existing approaches utilizing LLMs/MLLMs have not fully tapped into the crucial information provided by document layouts. To address this gap, a novel method called LayoutLLM is introduced in this paper. At the heart of LayoutLLM lies a layout instruction tuning strategy specifically crafted to enhance the comprehension and utilization of document layouts. This strategy comprises two key components: Layout-aware Pre-training and Layout-aware Supervised Fine-tuning. focuses on three sets of pre-training tasks to capture nuances of document layout at different levels: document-level, region-level, and segment-level information. This allows for a more comprehensive understanding of document layouts. further enhances the performance of LayoutLLM by incorporating a groundbreaking module called . This module enables LayoutLLM to focus on relevant regions and generate precise answers based on the query at hand. Experimental results on standard benchmarks demonstrate that  that rely on open-source 7B LLMs/MLLMs for document understanding significantly. Moreover, the training data for LayoutLLM is made publicly available at https://github.com/AlibabaResearch/AdvancedLiterateMachinery/tree/main/DocumentUnderstanding/LayoutLLM. The authors Chuwei Luo, Yufan Shen, Zhaoqing Zhu, Qi Zheng, Zhi Yu, and Cong Yao present their findings in this paper titled "LayoutLLM: Layout Instruction Tuning with Large Language Models for Document Understanding," which was submitted to CVPR 2024 conference.