Open Internet by MindsNet

Improving Long-Document QA for Image-Heavy PDFs

Current methods for long-document QA, especially those involving image-heavy PDFs with charts, images, and tables, face significant accuracy challenges. Vision-capable LLMs and OCR-based pipelines have limitations, including lower accuracy on chart-heavy and table-heavy pages, high costs, and intrinsic failure rates. There is a need for more reliable and cost-effective solutions.

Computing & Technology, Computer Science, Artificial Intelligence