3 papers
cs.CL2025
EncouRAGe: Evaluating RAG Local, Fast, and Reliable
Jan Strich, Adeline Scharfenberg, Chris Biemann +1
We introduce EncouRAGe, a comprehensive Python framework designed to streamline the development and evaluation of Retrieval-Augmented Generation (RAG) systems using Large Language…
cs.CV2025
MTabVQA: Evaluating Multi-Tabular Reasoning of Language Models in Visual Space
Anshul Singh, Chris Biemann, Jan Strich
Vision-Language Models (VLMs) have demonstrated remarkable capabilities in interpreting visual layouts and text. However, a significant challenge remains in their ability to interp…
cs.IR2025
T-RAGBench: Text-and-Table Benchmark for Evaluating Retrieval-Augmented Generation
Jan Strich, Enes Kutay Isgorur, Maximilian Trescher +2
Since many real-world documents combine textual and tabular data, robust Retrieval Augmented Generation (RAG) systems are essential for effectively accessing and analyzing such con…