PDF-WuKong: A Large Multimodal Model for Efficient Long PDF Reading with End-to-End Sparse Sampling
<|reference_start|>PDF-WuKong: A Large Multimodal Model for Efficient Long PDF Reading with End-to-End Sparse Sampling: Document understanding is a challenging task to process and comprehend large amounts of textual and visual information. Recent advances in Large Language Models (LLMs) have significantly improved the performance of this task. However, existing methods typically focus on either plain text or a limited number of document images, struggling to handle long PDF documents with interleaved text and images, especially in academic papers. In this paper, we introduce PDF-WuKong, a multimodal large language model (MLLM) which is designed to enhance multimodal question-answering (QA) for long PDF documents. PDF-WuKong incorporates a sparse sampler that operates on both text and image representations, significantly improving the efficiency and capability of the MLLM. The sparse sampler is integrated with the MLLM's image encoder and selects the paragraphs or diagrams most pertinent to user queries for processing by the language model. To effectively train and evaluate our model, we construct PaperPDF, a dataset consisting of a broad collection of academic papers sourced from arXiv, multiple strategies are proposed to generate automatically 1M QA pairs along with their corresponding evidence sources. Experimental results demonstrate the superiority and high efficiency of our approach over other models on the task of long multimodal PDF understanding, surpassing proprietary products by an average of 8.6% on F1. Our code and dataset will be released at https://github.com/yh-hust/PDF-Wukong.<|reference_end|>
@article{xie2024pdf-wukong:,
    title={PDF-WuKong: A Large Multimodal Model for Efficient Long PDF Reading with
  End-to-End Sparse Sampling},
    author={Xudong Xie, Liang Yin, Hao Yan, Yang Liu, Jing Ding, Minghui Liao,
  Yuliang Liu, Wei Chen, Xiang Bai},
    journal={arXiv preprint arXiv:2410.05970},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.05970},
    primaryClass={cs.CV cs.AI cs.CL}
}