Empirical Evaluation of PDF Parsing and Chunking for Financial Question Answering with RAG
New benchmark shows specific chunking strategies can boost financial document accuracy by 40%.
A team of eight researchers from multiple institutions has published the first comprehensive study on how PDF parsing and chunking choices affect Retrieval-Augmented Generation (RAG) systems for financial document analysis. The paper, "Empirical Evaluation of PDF Parsing and Chunking for Financial Question Answering with RAG," systematically tests various PDF parsers and chunking strategies with different overlap settings to determine optimal configurations for preserving document structure and ensuring answer accuracy. The researchers introduced TableQuest, a newly generated and publicly available benchmark specifically designed for financial document question answering.
The study's key contribution lies in its practical guidelines for building robust RAG pipelines that handle the heterogeneous content of financial PDFs—including complex tables, text, and images. By examining the synergies between different parsing tools and chunking approaches, the research provides data-driven recommendations that could significantly improve the performance of financial AI assistants. The 12-page paper represents a major step toward standardizing best practices for extracting structured information from PDFs, which remain notoriously difficult for automated systems to process accurately.
- Introduces TableQuest, a new publicly available benchmark for financial document Q&A
- Systematically evaluates multiple PDF parsers and chunking strategies with varied overlap settings
- Provides practical guidelines for building robust RAG pipelines that preserve document structure
Why It Matters
Enables more accurate AI financial analysis by establishing best practices for extracting data from complex PDF documents.