Retrieval-Augmented Generation (RAG) enhances Large Language Models (LLMs) by incorporating information retrieved from external data sources. It uses the retrieved data as a reference to organize answers, significantly improving the accuracy and relevance of responses, and effectively addressing issues such as hallucinations, making it particularly well-suited for knowledge-intensive tasks. RAG has experienced rapid expansion with the rise of LLM and has emerged as one of the most prominent technologies for enhancing and implementing LLM applications. It holds significant importance for the advancement of generative AI. To address the challenges and opportunities in RAG, this special issue has established a platform to bring together exceptional ideas and advanced technological research outcomes from the global research and industrial communities. The aim of this special issue is to highlight recent advances and challenges in the development of RAG techniques, trends, and applications. Prospective submissions may fall into, but are not limited to the following topics:

• RAG optimization method
• RAG evaluation framework
• Role of LLMs in RAG
• RAG Robustness
• Large retrieval-augmented language model
• RAG under large scale data
• Applications of RAG
• Retrieval-augmented Multi-modal model
• Data-centered Retrieval Augmentation
• Retrieval and long-context
• Memory in RAG System
• Production-ready RAG
• Data governance in RAG system
• Privacy data protection in RAG system

  The authors are requested to submit their full research papers complying with the general scope of the journal. The submitted papers will undergo peer review process before they can be accepted. Notification of acceptance will be communicated as we progress with the review process.

SUBMISSION GUIDELINES

Papers submitted to this journal for possible publication must be original and must not be under consideration for publication in any other journals. Prospective authors should submit an electronic copy of their completed manuscript to https://mc03.manuscriptcentral.com/bdma with manuscript type as “Special Issue on Challenges and Opportunities in Retrieval-Augmented Generation for LLMs: Techniques, Trends and Applications”. Further information on the journal is available at: https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=8254253.

IMPORTANT DATES

Deadline for submissions:  Feb 15^th 2025

GUEST EDITORS

Prof. Philip S. Yu, University of Illinons at Chicago, USA. E-mail: psyu@cs.uic.edu.

Prof. Haofen Wang, Tongji University, China. E-mail: haofen.wang@tongji.edu.cn.

Prof. Feida Zhu, Singapore Management University, Singapore. E-mail: fdzhu@smu.edu.sg.