InstructDoc: A Dataset for Zero-Shot Generalization of Visual Document
  Understanding with Instructions
Ryota Tanaka Taichi Iki Kyosuke Nishida Kuniko Saito Jun Suzuki
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.