Instructdoc: A Dataset For Zero-shot Generalization Of Visual Document Understanding With Instructions

Tanaka Ryota, Iki Taichi, Nishida Kyosuke, Saito Kuniko, Suzuki Jun. Arxiv 2024

[Paper]    
Applications GPT Model Architecture Multimodal Models Reinforcement Learning Training Techniques

We study the problem of completing various visual document understanding (VDU) tasks, e.g., question answering and information extraction, on real-world documents through human-written instructions. To this end, we propose InstructDoc, the first large-scale collection of 30 publicly available VDU datasets, each with diverse instructions in a unified format, which covers a wide range of 12 tasks and includes open document types/formats. Furthermore, to enhance the generalization performance on VDU tasks, we design a new instruction-based document reading and understanding model, InstructDr, that connects document images, image encoders, and large language models (LLMs) through a trainable bridging module. Experiments demonstrate that InstructDr can effectively adapt to new VDU datasets, tasks, and domains via given instructions and outperforms existing multimodal LLMs and ChatGPT without specific training.

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