LongDocURL: a Comprehensive Multimodal Long Document Benchmark Integrating Understanding, Reasoning, and Locating
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·December 24, 2024
Summary
The LongDocURL benchmark, introduced in 2018, integrates long document understanding, numerical reasoning, and cross-element locating tasks. It comprises 20 sub-tasks across three primary categories, addressing complex document elements, longer contexts, and diverse tasks. The benchmark includes 2,325 high-quality question-answering pairs from over 33,000 pages, significantly surpassing existing benchmarks. Comprehensive evaluation experiments across 26 configurations reveal critical performance gaps in the field.
Introduction
Background
Origin and development of the LongDocURL benchmark
Importance of long document understanding in AI research
Objective
Aim of the LongDocURL benchmark in advancing AI capabilities
Key challenges addressed by the benchmark
Benchmark Structure
Categories
Explanation of the three primary categories: complex document elements, longer contexts, and diverse tasks
Sub-tasks
Overview of the 20 sub-tasks included in the benchmark
Detailed description of each sub-task and its relevance
Dataset Composition
Data Volume
Total number of pages and question-answering pairs
Quality Assurance
Methods for ensuring high-quality data
Diversity and Complexity
Explanation of how the dataset addresses various complexities and diversities in long documents
Evaluation Experiments
Configurations
Description of the 26 configurations used in the evaluation
Performance Gaps
Identification of critical performance gaps in the field
Analysis of the implications of these gaps for future research
Conclusion
Summary of Findings
Recap of the benchmark's contributions and findings
Future Directions
Suggestions for future research based on the benchmark's insights
Basic info
papers
computation and language
artificial intelligence
Advanced features
Insights
What is the LongDocURL benchmark and when was it introduced?
How many high-quality question-answering pairs does the LongDocURL benchmark include?
What are the three primary categories of sub-tasks in the LongDocURL benchmark?
What does the comprehensive evaluation experiments across 26 configurations reveal about the field?