Are you looking for a way to automatically interpret and analyze enterprise documents such as contracts, reports, invoices, and receipts? Then you’ll be delighted to hear about the groundbreaking research conducted by JPMorgan AI Research, which has developed DocLLM – a lightweight version of conventional Large Language Models (LLMs) tailored for generative reasoning over documents with rich layouts!
DocLLM is inherently multi-modal, representing both text semantics and spatial layouts. To make processing faster and reduce model size, the team has removed the requirement for a sophisticated visual encoder and instead uses bounding box coordinates acquired through optical character recognition (OCR). DocLLM also extended the traditional transformers’ self-attention mechanism to capture cross-modal interactions, as well as infilling to accommodate various text arrangements and cohesive text blocks.
Impressively, the changes made by DocLLM have yielded notable performance gains of up to 61% in four of the five previously unpublished datasets. Furthermore, the team has designed a specialized instruction-tuning dataset for visual document intelligence tasks, which should be curated to fine-tune the model effectively.
The findings of this study are incredibly exciting and prove that DocLLM is a powerful solution for document intelligence tasks such as form comprehension, table alignment, and visual question responding. So, don’t miss out on this incredible opportunity to make the most of this cutting-edge technology. Check out the paper and join our 35k+ ML SubReddit, 41k+ Facebook Community, Discord Channel, LinkedIn Group, Twitter, and Email Newsletter to stay up to date on the latest AI research news, cool AI projects, and more!
