Summary of Layoutllm: Large Language Model Instruction Tuning For Visually Rich Document Understanding, by Masato Fujitake
LayoutLLM: Large Language Model Instruction Tuning for Visually Rich Document Understanding
by Masato Fujitake
First submitted to arxiv on: 21 Mar 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes LayoutLLM, a novel method for understanding imaged documents that integrates large-scale language models (LLMs) and existing research in document image understanding. The goal is to enhance document comprehension by incorporating awareness of images, text, and layout structure. Unlike existing methods, this approach does not require fine-tuning for each task and dataset, making it more efficient and cost-effective. The proposed model is fine-tuned with multimodal instruction datasets and shows improvement over the baseline model in various document analysis tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us better understand images of documents, which is important because we can use this technology to help machines read documents that are scanned or photographed. Right now, there are some ways for computers to do this, but they’re not very good and require a lot of training. The new method, called LayoutLLM, uses big language models to improve the accuracy of document analysis tasks like classification and information extraction. |
Keywords
* Artificial intelligence * Classification * Fine tuning
