标题： POINTS-Reader：用于文档转换的视觉-语言模型的无蒸馏适应 显示英文标题

标题： POINTS-Reader: Distillation-Free Adaptation of Vision-Language Models for Document Conversion

摘要： 高质量的标注数据对于训练准确的文档转换模型至关重要，特别是在表格、公式和多列文本等复杂格式的领域。 然而，手动标注既昂贵又耗时，而使用现有模型进行自动标注在处理这些具有挑战性的情况时往往缺乏准确性。 因此，通过蒸馏教师模型的输出来训练学生模型，可能会显著限制其在实际应用中的性能。 在本文中，我们提出了一种完全自动化、无需蒸馏的框架，包括两个阶段，用于构建能够处理各种文档格式和布局的高质量文档提取数据集和模型。 在第一阶段，我们引入了一种生成大规模、多样化的合成数据的方法，这使得模型能够在统一格式中提取关键元素，并具有强大的初始性能。 在第二阶段，我们提出了一种自我改进的方法，进一步将最初在合成数据上训练的模型适应到现实世界的文档中。 具体来说，我们首先使用微调后的模型对真实文档进行标注，然后应用一套过滤策略来验证标注质量，最后在验证后的数据集上重新训练模型。 通过反复重复这一过程，我们逐步提高模型的转换能力和生成数据的质量。 我们训练了一个公开的 POINTS-1.5 模型以获得 POINTS-Reader，该模型超越了许多现有公共和专有模型，这些模型的规模相当或更大。 我们的模型可在 https://github.com/Tencent/POINTS-Reader 获取。 显示英文摘要

摘要： High-quality labeled data is essential for training accurate document conversion models, particularly in domains with complex formats such as tables, formulas, and multi-column text. However, manual annotation is both costly and time-consuming, while automatic labeling using existing models often lacks accuracy in handling such challenging scenarios. Consequently, training student models by distilling outputs from teacher models can significantly limit their performance in real-world applications. In this paper, we propose a fully automated, distillation-free framework comprising two stages for constructing high-quality document extraction datasets and models capable of handling diverse document formats and layouts. In the first stage, we introduce a method for generating large-scale, diverse synthetic data, which enables a model to extract key elements in a unified format with strong initial performance. In the second stage, we present a self-improvement approach that further adapts the model, initially trained on synthetic data, to real-world documents. Specifically, we first use the fine-tuned model to annotate real documents, then apply a suite of filtering strategies to verify annotation quality, and finally retrain the model on the verified dataset. By iteratively repeating this process, we progressively enhance both the model's conversion capabilities and the quality of the generated data. We train a public POINTS-1.5 model to obtain POINTS-Reader, which surpasses many existing public and proprietary models of comparable or larger size. Our model is available at https://github.com/Tencent/POINTS-Reader.