R 3 AG: First Workshop on Refined and Reliable Retrieval Augmented Generation

Annual International ACM SIGIR Conference on Research and Development in Information Retrieval in the Asia Pacific Region (SIGIR-AP), pp. 307–310

Abstract

Retrieval-augmented generation (RAG) has gained wide attention as the key component to improve generative models with external knowledge augmentation from information retrieval. It has shown great prominence in enhancing the functionality and performance of large language model (LLM)-based applications. However, with the comprehensive application of RAG, more and more problems and limitations have been identified, thus urgently requiring further fundamental exploration to improve current RAG frameworks. This workshop aims to explore in depth how to conduct refined and reliable RAG for downstream AI tasks.

Authors 7

1. Zihan Wang

   University of Amsterdam

   Affiliation as printed

   University of Amsterdam, Amsterdam, Netherlands

2. Xuri Ge

   University of Glasgow

   Affiliation as printed

   University of Glasgow, Glasgow, United Kingdom

3. Joemon M. Jose

   University of Glasgow

   Affiliation as printed

   University of Glasgow, Glasgow, United Kingdom

4. Hai-Tao Yu

   University of Tsukuba

   Affiliation as printed

   University of Tsukuba, Tsukuba, Japan

5. Weizhi Ma

   Tsinghua University

   Affiliation as printed

   Tsinghua University, Beijing, China

6. Zhaochun Ren Aachen

   Leiden University

   Affiliation as printed

   Leiden University, Leiden, Netherlands

7. Xin Xin

   Shandong University

   Affiliation as printed

   Shandong University, Qingdao, China