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. We call for participants to re-examine and formulate the basic principles and practical implementation of refined and reliable RAG. The workshop serves as a platform for both academia and industry researchers to conduct discussions, share insights, and foster research to build the next generation of RAG systems. Participants will engage in discussions and presentations focusing on fundamental challenges, cutting-edge research, and potential pathways to improve RAG.
We invite researchers to submit their latest work to the R3AG workshop on fundamental challenges associated with the RAG pipeline. The topics of interest include, but are not limited to:
Submissions of papers must be at least 2 pages and at most 9 pages (including figures, tables, proofs, appendixes, acknowledgments, and any content except references) in length, with unlimited pages for references. Submissions of papers must be in English, in PDF format, in the current ACM two-column conference format. Suitable LaTeX, Word, and Overleaf templates are available from the ACM Website (use the “sigconf” proceedings template for LaTeX and the Interim Template for Word).
All submissions of papers must be original and have not been published or accepted elsewhere or simultaneously submitted to another journal or conference. The review process of the submitted manuscripts will be done together with our program committee. The selection will depend on the technical soundness and relevance of submissions to the community that the workshop is targeting.
At least one author of each accepted paper must attend the workshop on-site and present their work. Submissions must be anonymous and should be submitted electronically via EasyChair: https://easychair.org/conferences/?conf=r3agsigirap2024.
TBA
Zihan Wang, University of Amsterdam, zihanwang.sdu@gmail.com
Xuri Ge, University of Glasgow, x.ge.2@research.gla.ac.uk
Joemon M. Jose, University of Glasgow, joemon.jose@glasgow.ac.uk
Haitao Yu, University of Tsukuba, yuhaitao@slis.tsukuba.ac.jp
Weizhi Ma, Tsinghua University, mawz12@hotmail.com
Zhaochun Ren, Leiden University, z.ren@liacs.leidenuniv.nl
Xin xin, Shandong University, xinxin@sdu.edu.cn