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 delve into 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 at having a clearer understanding of how robust information retrieval mechanisms can significantly enhance the capabilities of RAG systems.

Participants will engage in discussions and presentations focusing on the latest research, challenges, and potential pathways for advancing the information retrieval component within 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.

Submission Guidelines

All submissions will be peer reviewed (double-blind) by the program committee and judged by their relevance to the workshop, especially to the main themes identified below, and their potential to generate discussion. All submission must be written in English and formatted according to the latest ACM SIG proceedings template available at http://www.acm.org/publications/proceedings-template. 

Submissions must describe work that is not previously published, not accepted for publication elsewhere, and not currently under review elsewhere.

The workshop follows a double-blind reviewing process. Please note that at least one of the authors of each accepted paper must register for the workshop and present the paper. 

We invite research contributions, position, demo and opinion papers. Submissions must either be short (at most 4 pages) or full papers (at most 9 pages). References do not count against the page limit.

We encourage but do not require authors to release any code and/or datasets associated with their paper.

List of Topics

We invite submissions related to (but not limited to):

• Use Of The Retrieved Context By The LLM: Recent work has demonstrated that RAG systems are sensible to the order and the nature of the retrieved context. These can be considered preliminary results that pave the way for future research.
• (Query) Representation Learning: Improving how queries are represented can significantly enhance the retriever's ability to find relevant documents. This could involve using more advanced natural language processing techniques to understand the context and nuances of the query better.
• Incorporating Contextual Information: Including more context in the retrieval process can improve the relevance of the documents retrieved. This could mean taking into account the broader conversation, user preferences, or historical interactions
• Updating the Document Database: Keeping the document database up-to-date ensures that the retriever has access to the latest and most relevant information. This is particularly important for topics that are rapidly evolving.
• Reducing Computational Load: Optimizing the retriever for speed and efficiency, especially when dealing with large databases, can improve its usability in real-time applications. This might involve techniques for reducing the dimensionality of data or faster search algorithms.
• Bias Mitigation: Actively working to identify and mitigate biases in the retrieval process can improve the fairness and reliability of the retrieved content.
• Cross-Lingual Retrieval Capabilities: For systems operating in multilingual environments, improving the retriever's ability to handle and retrieve documents in various languages can enhance its effectiveness.
• Multimodality: Most of the current research has focused on textual RAG, even though multimodality is highly needed in many applications.
• Other: One of the goals of this workshop is to collect new ideas and challenges, so proposals in this sense are very much welcomed.

Organizing Committee

• Fabio Petroni, Samaya AI
• Federico Siciliano, Sapienza University of Rome
• Fabrizio Silvestri, Sapienza University of Rome
• Giovanni Trappolini, Sapienza University of Rome

Invited Speakers

• Nicola Tonellotto, University of Pisa
• Yuhao Zhang, Samaya AI

Contact

All questions about submissions should be emailed to ir.rag.sigir@gmail.com.