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DocPrompt: Large-scale continue pretrain for zero-shot and few-shot document question answering

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In this paper, we propose Docprompt for document question answering tasks with powerful zero-shot and few-shot performance. We proposed a novel weakly supervised data generation method, a novel multl-stage training method and a novel understanding model \& generation model ensemble method. We achieved state-of-the-art performance on 4 document question answering tasks. This method greatly improves the delivery efficiency and model performance of document question answering customer projects, reducing annotation costs and labor costs. Our demo can be found at https://huggingface.co/spaces/PaddlePaddle/ERNIE-Layout.

Sijin Wu, Dan Zhang, Teng Hu, Shikun Feng• 2023

Related benchmarks

TaskDatasetResultRank
Question AnsweringWebSRC (test)
EM77.4
17
Visual Question AnsweringWebSRC (test)
Accuracy0.85
4
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