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

• URL: http://arxiv.org/abs/2403.14252v1
• Date: Thu, 21 Mar 2024 09:25:24 GMT
• Title: LayoutLLM: Large Language Model Instruction Tuning for Visually Rich Document Understanding
• Authors: Masato Fujitake,
• Abstract summary: This paper proposes LayoutLLM, a more flexible document analysis method for understanding imaged documents. Existing methods have been developed to enhance document comprehension by incorporating pre-training awareness of images, text, and layout structure. Our experiments demonstrate improvement over the baseline model in various document analysis tasks.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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