Summary of Longdocurl: a Comprehensive Multimodal Long Document Benchmark Integrating Understanding, Reasoning, and Locating, by Chao Deng et al.
LongDocURL: a Comprehensive Multimodal Long Document Benchmark Integrating Understanding, Reasoning, and Locating
by Chao Deng, Jiale Yuan, Pi Bu, Peijie Wang, Zhong-Zhi Li, Jian Xu, Xiao-Hui Li, Yuan Gao, Jun Song, Bo Zheng, Cheng-Lin Liu
First submitted to arxiv on: 24 Dec 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a new benchmark for document understanding called LongDocURL, which integrates three primary tasks: Long Document Understanding, numerical Reasoning, and cross-element Locating. The benchmark consists of 20 sub-tasks that cover different aspects of document analysis. To develop the benchmark, the authors created a semi-automated construction pipeline and collected over 2,300 high-quality question-answer pairs covering more than 33,000 pages of documents. The authors also conducted comprehensive evaluation experiments on various models, revealing performance gaps in this field. This paper aims to improve the document understanding capabilities of large vision language models (LVLMs) by providing a comprehensive analysis of layout elements and handling complex document elements. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new benchmark for understanding documents called LongDocURL. It’s like a big test to see how well computers can understand what’s in a long document. The test has many small parts that ask the computer to do different things, like find specific information or do math problems. To make this test, the authors collected lots of questions and answers from real documents. They then tested different computer models on this test and found out that some models are really good at understanding documents, but others need more work. |
