LayoutLLM: Large Language Model Instruction Tuning for Visually Rich Document Understanding
This work addresses the need for more flexible and efficient document analysis methods for applications like classification and information extraction, though it appears incremental as it builds on existing multimodal and LLM approaches.
The paper tackles the problem of expensive and task-specific fine-tuning in Visually Rich Document Understanding by proposing LayoutLLM, which integrates document image understanding with large language models through multimodal instruction tuning, resulting in improved performance over baselines across various tasks.
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.