Armineh Nourbakhsh from JP Morgan AI Research discusses the development of DocLLM, a layout-aware large language model for document understanding. Topics include challenges of document AI, training approaches, datasets used, incorporating layout information, and evaluating model performance.
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Quick takeaways
DocLLM integrates textual semantics and spatial layout for processing complex documents.
Armineh emphasizes the importance of instruction tuning and future directions for DocLLM's development.
Deep dives
Armina's Background and Introduction to NLP
Armina shares her background in Unimodal NLP and how she got into AI research. She accidentally ended up working on sentiment analysis and later transitioned to document AI.
Understanding the Document AI Landscape
Armina discusses the focus of document AI on enterprise documents, differentiating them from documents like restaurant menus. She highlights the importance of information extraction and the challenges of incorporating spatial and textual information.
Introducing Doc LLM and its Objectives
Armina introduces Doc LLM, a pre-trained model focused on understanding structured documents. She discusses the motivation behind its development, aiming to address limitations of encoder-only architectures, scalability, and fine-tuning data set size.
Approach and Contributions of Doc LLM
Armina explains the architectural modifications made to incorporate spatial information and the disentanglement of spatial and textual representations. She highlights the significance of instruction tuning and shares future directions, including visual information integration and improving robustness to hallucinations and verbal reasoning challenges.
Today we're joined by Armineh Nourbakhsh of JP Morgan AI Research to discuss the development and capabilities of DocLLM, a layout-aware large language model for multimodal document understanding. Armineh provides a historical overview of the challenges of document AI and an introduction to the DocLLM model. Armineh explains how this model, distinct from both traditional LLMs and document AI models, incorporates both textual semantics and spatial layout in processing enterprise documents like reports and complex contracts. We dig into her team’s approach to training DocLLM, their choice of a generative model as opposed to an encoder-based approach, the datasets they used to build the model, their approach to incorporating layout information, and the various ways they evaluated the model’s performance.
The complete show notes for this episode can be found at twimlai.com/go/672.
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