DocLLM: Revolutionizing Visual Document Interpretation with Multi-Modal AI

DocLLM: A Lightweight Language Model for Visual Document Interpretation

JPMorgan AI Research has introduced DocLLM, a lightweight version of conventional Large Language Models (LLMs) that takes into account both textual semantics and spatial layout and has been specifically created for reasoning over visual documents. This multi-modal model uses bounding box coordinates acquired using optical character recognition (OCR) to add spatial layout information, making it more efficient and reducing the need for a sophisticated visual encoder.

The pre-trained knowledge of DocLLM has been fine-tuned on instruction data from many datasets to suit different document intelligence jobs. These tasks include document categorization, visual question answering, natural language inference, and key information extraction. Both single- and multi-page documents have been covered by the instruction-tuning data, and layout cues like field separators, titles, and captions can be included to make it easier for readers to understand the papers’ logical structure.

The changes made by DocLLM have yielded notable performance gains, ranging from 15% to 61%, in four of the five previously unpublished datasets. The primary contributions of the study include introducing a lightweight extension of LLM for visual document interpretation, providing a unique attention mechanism, outlining a pre-training goal, designing a specialized instruction-tuning dataset, and performing in-depth trials to understand the model’s behavior and functions while managing visual documents.

For more information, check out the paper. If you like this kind of research, you can join the ML SubReddit, Facebook Community, Discord Channel, LinkedIn Group, Twitter and Email Newsletter.

Source link

Stay in the Loop

Get the daily email from AI Headliner that makes reading the news actually enjoyable. Join our mailing list to stay in the loop to stay informed, for free.

Latest stories

You might also like...