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LayoutLLM: Large Language Model Instruction Tuning for Visually Rich Document Understanding

Masato Fujitake

TL;DR

This paper proposes LayoutLLM, a more flexible document analysis method for understanding imaged documents that integrates these with large-scale language models (LLMs) and performs an understanding of document images in a single model.

Abstract

This paper proposes LayoutLLM, a more flexible document analysis method for understanding imaged documents. Visually Rich Document Understanding tasks, such as document image classification and information extraction, have gained significant attention due to their importance. Existing methods have been developed to enhance document comprehension by incorporating pre-training awareness of images, text, and layout structure. However, these methods require fine-tuning for each task and dataset, and the models are expensive to train and operate. To overcome this limitation, we propose a new LayoutLLM that integrates these with large-scale language models (LLMs). By leveraging the strengths of existing research in document image understanding and LLMs' superior language understanding capabilities, the proposed model, fine-tuned with multimodal instruction datasets, performs an understanding of document images in a single model. Our experiments demonstrate improvement over the baseline model in various document analysis tasks.

LayoutLLM: Large Language Model Instruction Tuning for Visually Rich Document Understanding

TL;DR

This paper proposes LayoutLLM, a more flexible document analysis method for understanding imaged documents that integrates these with large-scale language models (LLMs) and performs an understanding of document images in a single model.

Abstract

This paper proposes LayoutLLM, a more flexible document analysis method for understanding imaged documents. Visually Rich Document Understanding tasks, such as document image classification and information extraction, have gained significant attention due to their importance. Existing methods have been developed to enhance document comprehension by incorporating pre-training awareness of images, text, and layout structure. However, these methods require fine-tuning for each task and dataset, and the models are expensive to train and operate. To overcome this limitation, we propose a new LayoutLLM that integrates these with large-scale language models (LLMs). By leveraging the strengths of existing research in document image understanding and LLMs' superior language understanding capabilities, the proposed model, fine-tuned with multimodal instruction datasets, performs an understanding of document images in a single model. Our experiments demonstrate improvement over the baseline model in various document analysis tasks.
Paper Structure (11 sections, 2 figures, 5 tables)

This paper contains 11 sections, 2 figures, 5 tables.

Table of Contents

  1. Introduction
  2. Related Works
  3. Method
  4. Experiments
  5. Dataset and Evaluation
  6. Implementation Details
  7. Main Results
  8. Detailed Analysis
  9. Conclusion
  10. Bibliographical References
  11. Language Resource References

Figures (2)

  • Figure 1: The overview of the existing and proposed method approaches. The current method is fine-tuned for each task after pre-training, as shown in (a). In contrast, our method is fine-tuned via LLMs to handle multiple tasks, as shown in (b).
  • Figure 2: Architectural overview of the proposed LayoutLLM. It consists mainly of an encoder that encodes document images and a decoder that interprets tasks, and outputs.