R3AG 2025: The Second Workshop on Refined and Reliable Retrieval-Augmented Generation

Open Access
Authors
  • Haitao Yu
  • Yubo Fang
  • Xuri Ge
  • Xin Xin
  • Zihan Wang
  • Junchen Fu
  • Joemon M. Jose
  • Weizhi Ma
  • Zhaochun Ren
Publication date 2025
Book title SIGIR-AP 2025
Book subtitle Proceedings of the 2025 Annual International ACM SIGIR Conference on Research and Development in Information Retrieval in the Asia Pacific Region : December 7-10, 2025, Xi'an, China
ISBN (electronic)
  • 9798400722189
Event 3rd International ACM SIGIR Conference on Research and Development in Information Retrieval in the Asia Pacific Region, SIGIR-AP 2025
Pages (from-to) 461-464
Number of pages 4
Publisher New York, NY: Association for Computing Machinery
Organisations
  • Faculty of Science (FNWI) - Informatics Institute (IVI)
Abstract

In recent years, large language models (LLMs) have demonstrated remarkable capabilities across a broad spectrum of tasks, spanning language understanding, complex reasoning, and decision-making. However, they still face inherent limitations, such as hallucinations and outdated parametric knowledge. To mitigate these challenges, retrieval-augmented generation (RAG) has emerged as a promising technique and has attracted increasing attention. As RAG continues to be widely applied, an increasing number of challenges and limitations have surfaced, underscoring the urgent need for deeper, foundational research to advance and refine current RAG frameworks. Therefore, we propose to organize R³AG 2025, the second workshop on Refined and Reliable Retrieval-Augmented Generation, at SIGIR-AP 2025. This workshop seeks to bring together researchers and practitioners to re-examine and re-establish the core principles and practical implementations of refined and reliable RAG. This workshop will serve as a collaborative platform for academia and industry to exchange insights, discuss foundational issues and recent advancements. By the end of the workshop, we aim to arrive at a clearer understanding on promising directions for enhancing the reliability and applicability of RAG.

Document type Conference contribution
Language English
Published at https://doi.org/10.1145/3767695.3769524
Other links https://www.scopus.com/pages/publications/105026261011
Downloads
3767695.3769524 (Final published version)
Permalink to this page
Back