IR-RAG@ SIGIR24: Information Retrieval's Role in RAG Systems

In recent years, Retrieval Augmented Generation (RAG) systems have emerged as a pivotal component in the field of artificial intelligence, gaining significant attention and importance across various domains. These systems, which combine the strengths of information retrieval and generative models, have shown promise in enhancing the capabilities and performance of machine learning applications. However, despite their growing prominence, RAG systems are not without their limitations and continue to be in need of exploration and improvement. This workshop seeks to focus on the critical aspect of information retrieval and its integral role within RAG frameworks. We argue that current efforts have undervalued the role of Information Retrieval (IR) in the RAG and have concentrated their attention on the generative part. As the cornerstone of these systems, IR's effectiveness dramatically influences the overall performance and outcomes of RAG models. We call for papers that will seek to revisit and emphasize the fundamental principles underpinning RAG systems. At the end of the workshop, we aim to have a clearer understanding of how robust information retrieval mechanisms can significantly enhance the capabilities of RAG systems. The workshop will serve as a platform for experts, researchers, and practitioners. We intend to foster discussions, share insights, and encourage research that underscores the vital role of Information Retrieval in the future of generative systems.