project
LongDocURL - A benchmark dataset for multimodal long document understanding jointly launched by the Chinese Academy of Sciences and Taotian Group.
LongDocURL is a multimodal long document understanding benchmark dataset jointly released by the Institute of Automation, Chinese Academy of Sciences, and Alibaba's Taobao Tmall Group. It focuses on evaluating models' performance in handling long documents, complex elements, and diverse tasks...
What is LongDocURL?
LongDocURL is a multimodal long document understanding benchmark dataset jointly released by the Institute of Automation, Chinese Academy of Sciences, and Alibaba's Taobao Tmall Group. It focuses on evaluating models' understanding, reasoning, and localization capabilities in handling long documents, complex elements, and diverse tasks. The dataset contains 2,325 question-answer pairs, covering over 33,000 pages of documents and involving 20 sub-tasks, aiming to advance document understanding technology.
The main functions of LongDocURL
- Long document comprehensionThe evaluation assesses the AI model's ability to understand complex text content, including extracting core information, identifying key paragraphs and details, and analyzing document structure such as headings, charts, and illustrations.
- Numerical ReasoningExamine the AI model's ability to process data and perform precise calculations, especially its ability to understand and process documents containing large amounts of numerical information, such as data in financial reports and scientific literature.
- Cross-element positioningThe evaluation of a model’s ability to locate and associate different types of elements (such as text, tables, and charts) in long documents is crucial for comprehension and reasoning tasks.
- Diverse tasksThe dataset is divided into 20 sub-tasks, covering three major tasks: understanding, reasoning, and localization, based on different task types and sources of evidence.
- Semi-automated build processThis includes steps such as document screening, question and answer generation, and automated and manual verification to ensure the quality and diversity of the dataset.
- Support for multiple document typesIt covers various document types, including research reports, user manuals, and books, with an average document length of 85.6 pages, providing a wide range of application scenarios.
The technical principles of LongDocURL
- Multimodal document understandingLongDocURL aims to evaluate a model's ability to handle long documents containing multiple modalities, such as text, images, and tables. This involves integrating different elements of the document, such as text and images, into a shared multimodal embedding space so that the model can understand and infer the relationships between these elements.
- Page search and question-and-answer generationLongDocURL uses multimodal retrieval models (such as ColPali) to retrieve the most relevant pages to the query, and uses multimodal language models (such as Qwen2-VL) to perform visual question answering on the retrieved page images and the query to generate the final answer.
- Semi-automated build processLongDocURL constructs datasets through a semi-automated workflow, comprising four modules: document extraction and filtering, question-answer generation, automated validation, and manual validation. This workflow efficiently generates high-quality question-answer pairs from a large number of documents while ensuring content quality.
- Model EvaluationLongDocURL presents a new benchmark comprising 2,441 multi-hop questions distributed across 3,368 PDF documents, totaling 41,005 pages. Each question is supported by evidence from one or more documents, encompassing various modalities such as text, images, and tables, capturing the typical complexity and diversity of real-world documents.
- Task ClassificationLongDocURL categorizes tasks into three main types: understanding, reasoning, and localization, and further subdivides them into 20 sub-tasks based on different main tasks and answer evidence, supporting more granular evaluation.
LongDocURL's project address
- Project address:longdocurl.github.io
- Github repository:https://github.com/dengc2023/LongDocURL
- arXiv technical paper:https://arxiv.org/pdf/2412.18424
Application scenarios of LongDocURL
- Document UnderstandingThe LongDocURL dataset can be used to evaluate and train AI models to understand long documents, including extracting key information and parsing document structure.
- Numerical ReasoningIn fields such as finance and accounting, LongDocURL can be used to train AI models for numerical calculations, comparisons, and summaries, and to process documents containing large amounts of numerical information.
- legal fieldIn the legal field, LongDocURL can help AI systems analyze large amounts of legal documents, providing case-related information extraction and evidence location.
- medical fieldLongDocURL can be used to analyze text records and image data in medical records to assist doctors in making more comprehensive diagnoses.
- Smart manufacturingIn the field of smart manufacturing, LongDocURL can be used to monitor the status of equipment on the production line and optimize the production process by combining operation manuals and sensor data.
- Scientific researchLongDocURL provides a standardized evaluation benchmark that helps improve the performance of models in scientific document understanding tasks, especially when dealing with structured scientific literature.