IR-RAG @SIGIR25: The Second Edition of the Workshop on Information Retrieval's Role in RAG Systems
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.
Authors
- Fabrizio Silvestri (ORCID: https://orcid.org/0000-0001-7669-9055)
- Negar Arabzadeh (ORCID: https://orcid.org/0000-0002-4411-7089)
- Federico Siciliano (ORCID: https://orcid.org/0000-0003-1339-6983)
- Giovanni Trappolini (ORCID: https://orcid.org/0000-0002-5515-634X)
- Fabio Petroni (ORCID: https://orcid.org/0009-0005-5996-9830)
- Ziheng Chen (ORCID: https://orcid.org/0000-0002-2585-637X)
Institutions
- Walmart (United States) (US)
- University of Waterloo (CA)
- Sapienza University of Rome (IT)
Publication Details
- Published
- 2025-07-13
- DOI
- https://doi.org/10.1145/3726302.3730362
- Citations
- 1
- Primary Topic
- Image Retrieval and Classification Techniques
- Type
- article
- Field-Weighted Citation Impact
- 1.28