IR-RAG @SIGIR25: The Second Edition of the Workshop on Information Retrieval's Role in RAG Systems
Negar Arabzadeh, Ziheng Chen, Fabio Petroni, Federico Siciliano, Fabrizio Silvestri, Giovanni Trappolini · 2025
In recent years, Retrieval-Augmented Generation (RAG) systems have become a cornerstone of artificial intelligence, attracting considerable attention in a variety of fields. By integrating the strengths of information retrieval and generative models, these systems have shown immense potential to push the boundaries of machine learning applications. Nevertheless, RAG systems still face significant challenges and offer ample room for advancement and innovation.