Document Understanding Challenges and Solutions
Practical Solutions and Value
Document understanding (DU) involves interpreting and processing complex documents containing text, tables, charts, and images. Extracting valuable information from lengthy, multi-modal documents is essential for various industries.
Understanding long-context documents spanning many pages is a critical challenge. Traditional single-page DU models struggle with this, making it crucial to develop benchmarks to evaluate models’ performance.
Current methods for DU involve Large Vision-Language Models (LVLMs) like GPT-4o, Gemini-1.5, and Claude-3. These models have shown promise on single-page tasks but need help with long-context document understanding due to the need for multi-page comprehension and integrating multimodal elements.
Researchers have introduced MMLongBench-Doc, a comprehensive benchmark to evaluate the long-context DU capabilities of LVLMs. This benchmark includes 135 PDF-formatted documents from diverse domains, featuring questions requiring evidence from text, images, charts, tables, and layout structures.
The study revealed that LVLMs generally struggle with long-context DU, underscoring the need for more advanced models. The detailed results highlighted the necessity for more capable LVLMs, and proprietary models outperformed open-source ones.
In conclusion, MMLongBench-Doc is a valuable tool for evaluating and improving DU models’ performance, highlighting the need for continued research and development in this area to achieve more effective and comprehensive DU solutions.
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