InstructDoc: A Dataset for Zero-Shot Generalization of Visual Document Understanding with Instructions
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866914650871824384 |
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| author | Tanaka, Ryota Iki, Taichi Nishida, Kyosuke Saito, Kuniko Suzuki, Jun |
| author_facet | Tanaka, Ryota Iki, Taichi Nishida, Kyosuke Saito, Kuniko Suzuki, Jun |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2401_13313 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | InstructDoc: A Dataset for Zero-Shot Generalization of Visual Document Understanding with Instructions Tanaka, Ryota Iki, Taichi Nishida, Kyosuke Saito, Kuniko Suzuki, Jun Computer Vision and Pattern Recognition Computation and Language 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. |
| title | InstructDoc: A Dataset for Zero-Shot Generalization of Visual Document Understanding with Instructions |
| topic | Computer Vision and Pattern Recognition Computation and Language |
| url | https://arxiv.org/abs/2401.13313 |