mlsanityData to 14 Oct 2026

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

arXiv 2308.10959 ↗First posted 21 Aug 2023cs.CL cs.AIcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 2 of its 12 references that are in the map. First posted on arXiv 21 Aug 2023.

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References · 2

  1. 2021LayoutXLM: Multimodal Pre-training for Multilingual Visually-rich Document Understandingcited by 103
  2. 2022ERNIE-mmLayout: Multi-grained MultiModal Transformer for Document Understandingcited by 9

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