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arXiv · 2506.18023

PP-DocBee2: Improved Baselines with Efficient Data for Multimodal Document Understanding

Kui Huang·Xinrong Chen·Wenyu Lv·Jincheng Liao·Guanzhong Wang·Yi Liu
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Abstract

This report introduces PP-DocBee2, an advanced version of the PP-DocBee, designed to enhance multimodal document understanding. Built on a large multimodal model architecture, PP-DocBee2 addresses the limitations of its predecessor through key technological improvements, including enhanced synthetic data quality, improved visual feature fusion strategy, and optimized inference methodologies. These enhancements yield an $11.4\%$ performance boost on internal benchmarks for Chinese business documents, and reduce inference latency by $73.0\%$ to the vanilla version. A key innovation of our work is a data quality optimization strategy for multimodal document tasks. By employing a large-scale multimodal pre-trained model to evaluate data, we apply a novel statistical criterion to filter outliers, ensuring high-quality training data. Inspired by insights into underutilized intermediate features in multimodal models, we enhance the ViT representational capacity by decomposing it into layers and applying a novel feature fusion strategy to improve complex reasoning. The source code and pre-trained model are available at \href{https://github.com/PaddlePaddle/PaddleMIX}{https://github.com/PaddlePaddle/PaddleMIX}.

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Kui Huang, Xinrong Chen, Wenyu Lv, Jincheng Liao, Guanzhong Wang, Yi Liu. 2025-06-22. PP-DocBee2: Improved Baselines with Efficient Data for Multimodal Document Understanding. https://arxiv.org/abs/2506.18023

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