Keskeiset käsitteet
Enhancing Multimodal Large Language Models with Unified Structure Learning for improved OCR-free Document Understanding.
Tiivistelmä
The content introduces the concept of Unified Structure Learning to enhance the performance of Multimodal Large Language Models in understanding text-rich images. It emphasizes the importance of structure information in Visual Document Understanding and proposes a vision-to-text module, H-Reducer, to efficiently encode structure information. The content details the two-stage training framework of DocOwl 1.5, highlighting its state-of-the-art performance on various benchmarks.
Directory:
- Introduction
- Importance of structure information in Visual Document Understanding.
- Data Extraction
- Key metrics supporting the proposed Unified Structure Learning.
- Quotations
- Striking quotes from the content.
- Inquiry and Critical Thinking
- Questions to broaden understanding and encourage analysis.
Tilastot
Our DocOwl 1.5 achieves state-of-the-art OCR-free performance on 10 Visual Document Understanding benchmarks.
Lainaukset
"Our model DocOwl 1.5 achieves state-of-the-art performance on 10 visual document understanding benchmarks."
"By performing joint training on DocReason25K and downstream datasets, DocOwl 1.5-Chat well balance giving a simple answer or detailed explanations."