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

• URL: http://arxiv.org/abs/2509.01215v1
• Date: Mon, 01 Sep 2025 07:54:18 GMT
• Title: POINTS-Reader: Distillation-Free Adaptation of Vision-Language Models for Document Conversion
• Authors: Yuan Liu, Zhongyin Zhao, Le Tian, Haicheng Wang, Xubing Ye, Yangxiu You, Zilin Yu, Chuhan Wu, Xiao Zhou, Yang Yu, Jie Zhou,
• Abstract summary: High-quality labeled data is essential for training accurate document conversion models.<n>We propose a fully automated framework comprising two stages for constructing high-quality document extraction datasets.<n>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.
• Score: 32.52489423671728
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• Approximating Language Model Training Data from Weights [70.08614275061689]
  We formalize the problem of data approximation from model weights and propose several baselines and metrics.<n>We develop a gradient-based approach that selects the highest-matching data from a large public text corpus.<n>Even when none of the true training data is known, our method is able to locate a small subset of public Web documents.
  arXiv  Detail & Related papers  (2025-06-18T15:26:43Z)
• DreamMask: Boosting Open-vocabulary Panoptic Segmentation with Synthetic Data [61.62554324594797]
  We propose DreamMask, which explores how to generate training data in the open-vocabulary setting, and how to train the model with both real and synthetic data.<n>In general, DreamMask significantly simplifies the collection of large-scale training data, serving as a plug-and-play enhancement for existing methods.<n>For instance, when trained on COCO and tested on ADE20K, the model equipped with DreamMask outperforms the previous state-of-the-art by a substantial margin of 2.1% mIoU.
  arXiv  Detail & Related papers  (2025-01-03T19:00:00Z)
• KaLM-Embedding: Superior Training Data Brings A Stronger Embedding Model [27.25688303240741]
  KaLM-Embedding is a general multilingual embedding model that leverages a large quantity of cleaner, more diverse, and domain-specific training data.<n>Our model has been trained with key techniques proven to enhance performance.
  arXiv  Detail & Related papers  (2025-01-02T03:17:51Z)
• LiveXiv -- A Multi-Modal Live Benchmark Based on Arxiv Papers Content [62.816876067499415]
  We propose LiveXiv: a scalable evolving live benchmark based on scientific ArXiv papers.<n>LiveXiv accesses domain-specific manuscripts at any given timestamp and proposes to automatically generate visual question-answer pairs.<n>We benchmark multiple open and proprietary Large Multi-modal Models (LMMs) on the first version of our benchmark, showing its challenging nature and exposing the models true abilities.
  arXiv  Detail & Related papers  (2024-10-14T17:51:23Z)
• An Integrated Data Processing Framework for Pretraining Foundation Models [57.47845148721817]
  Researchers and practitioners often have to manually curate datasets from difference sources. We propose a data processing framework that integrates a Processing Module and an Analyzing Module. The proposed framework is easy to use and highly flexible.
  arXiv  Detail & Related papers  (2024-02-26T07:22:51Z)
• Extensive Evaluation of Transformer-based Architectures for Adverse Drug Events Extraction [6.78974856327994]
  Adverse Event (ADE) extraction is one of the core tasks in digital pharmacovigilance. We evaluate 19 Transformer-based models for ADE extraction on informal texts. At the end of our analyses, we identify a list of take-home messages that can be derived from the experimental data.
  arXiv  Detail & Related papers  (2023-06-08T15:25:24Z)
• Dataless Knowledge Fusion by Merging Weights of Language Models [47.432215933099016]
  Fine-tuning pre-trained language models has become the prevalent paradigm for building downstream NLP models.<n>This creates a barrier to fusing knowledge across individual models to yield a better single model.<n>We propose a dataless knowledge fusion method that merges models in their parameter space.
  arXiv  Detail & Related papers  (2022-12-19T20:46:43Z)
• Efficient Training of Language Models to Fill in the Middle [17.118891860985123]
  We show that autoregressive language models can learn to infill text after we apply a straightforward transformation to the dataset. We use these ablations to prescribe strong default settings and best practices to train FIM models. We have released our best infilling model trained with best practices in our API, and release our infilling benchmarks to aid future research.
  arXiv  Detail & Related papers  (2022-07-28T17:40:47Z)
• Long Document Summarization with Top-down and Bottom-up Inference [113.29319668246407]
  We propose a principled inference framework to improve summarization models on two aspects. Our framework assumes a hierarchical latent structure of a document where the top-level captures the long range dependency. We demonstrate the effectiveness of the proposed framework on a diverse set of summarization datasets.
  arXiv  Detail & Related papers  (2022-03-15T01:24:51Z)
• Key Information Extraction From Documents: Evaluation And Generator [3.878105750489656]
  This research project compares state-of-the-art models for information extraction from documents. The results have shown that NLP based pre-processing is beneficial for model performance. The use of a bounding box regression decoder increases the model performance only for fields that do not follow a rectangular shape.
  arXiv  Detail & Related papers  (2021-06-09T16:12:21Z)
• Interpretable Entity Representations through Large-Scale Typing [61.4277527871572]
  We present an approach to creating entity representations that are human readable and achieve high performance out of the box. Our representations are vectors whose values correspond to posterior probabilities over fine-grained entity types. We show that it is possible to reduce the size of our type set in a learning-based way for particular domains.
  arXiv  Detail & Related papers  (2020-04-30T23:58:03Z)
• Abstractive Text Summarization based on Language Model Conditioning and Locality Modeling [4.525267347429154]
  We train a Transformer-based neural model on the BERT language model. In addition, we propose a new method of BERT-windowing, which allows chunk-wise processing of texts longer than the BERT window size. The results of our models are compared to a baseline and the state-of-the-art models on the CNN/Daily Mail dataset.
  arXiv  Detail & Related papers  (2020-03-29T14:00:17Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.