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We study the problem of completing various visual document understanding(VDU) tasks, e.g., question answering and information extraction, on real-worlddocuments through human-written instructions. To this end, we proposeInstructDoc, the first large-scale collection of 30 publicly available VDUdatasets, each with diverse instructions in a unified format, which covers awide range of 12 tasks and includes open document types/formats. Furthermore,to enhance the generalization performance on VDU tasks, we design a newinstruction-based document reading and understanding model, InstructDr, thatconnects document images, image encoders, and large language models (LLMs)through a trainable bridging module. Experiments demonstrate that InstructDrcan effectively adapt to new VDU datasets, tasks, and domains via giveninstructions and outperforms existing multimodal LLMs and ChatGPT withoutspecific training.

InstructDoc: A Dataset for Zero-Shot Generalization of Visual Document Understanding with Instructions