Papers by Masato Fujitake

1 papers
LayoutLLM: Large Language Model Instruction Tuning for Visually Rich Document Understanding (2024.lrec-main)

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Challenge: Existing methods to enhance document comprehension require fine-tuning for each task and dataset, and are expensive to train and operate.
Approach: They propose a more flexible document analysis method that integrates visual-rich document understanding with large-scale language models (LLMs) by leveraging existing research in document image understanding and LLMs’ superior language understanding capabilities, the proposed model performs an understanding of document images in a single model.
Outcome: The proposed model improves on the baseline model in document image understanding tasks.

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