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

• URL: http://arxiv.org/abs/2401.13313v1
• Date: Wed, 24 Jan 2024 09:09:37 GMT
• Title: InstructDoc: A Dataset for Zero-Shot Generalization of Visual Document Understanding with Instructions
• Authors: Ryota Tanaka, Taichi Iki, Kyosuke Nishida, Kuniko Saito, Jun Suzuki
• Abstract summary: InstructDoc is the first large-scale collection of 30 publicly available visual document understanding datasets. InstructDr 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.
• Score: 30.609533589284634
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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