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English(EN) Joint Training Is Not Enough: Conditioned Cross-Granularity Training for Multimodal Document Understanding

新训练方法提升多模态文档理解能力

研究人员调查了多模态文档理解中的互惠增强效应(MRE),特别是考察了将细粒度的跨度级任务与粗粒度的文档级任务相结合是否能提高性能。他们的研究使用了包括收据和商业表格在内的三个语料库,发现标准的联合训练并未带来显著改进,并且常常导致任务粒度之间的权衡。然而,一种新颖的条件化训练方法,在训练过程中将一个任务的输出纳入另一个任务的提示中,在三个数据集中的两个上显示出增强效果,尤其是在避免复杂文档集上的性能崩溃方面。 AI

影响 引入了一种新颖的训练技术,可以提高处理复杂文档的AI系统的准确性和鲁棒性。

排序理由 该集群包含一篇详细介绍多模态文档理解新训练方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CL 阅读 →

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新训练方法提升多模态文档理解能力

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该集群包含一篇详细介绍多模态文档理解新训练方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.CL TIER_1 English(EN) · Chengguang Gan, Yunhao Liang, Hanjun Wei, Qinghao Zhang, Shiwen Ni ·

    联合训练尚不足够:面向多模态文档理解的条件化跨粒度训练

    arXiv:2609.00756v1 Announce Type: new Abstract: The Mutual Reinforcement Effect (MRE) asks whether a fine, span-level and a coarse, document-level task help each other when one model handles both. We test it in multimodal document understanding on three corpora, two of receipts a…