Publication Type
Journal Article
Version
publishedVersion
Publication Date
1-2026
Abstract
Retrieval-Augmented Generation (RAG) represents a transformative advancement for Large Language Models (LLMs) by integrating external knowledge to substantially improve accuracy and mitigate hallucinations. As a pivotal technology in the contemporary generative Artificial Intelligence (AI) landscape, RAG addresses fundamental challenges in knowledge-intensive tasks. This special issue serves as a dedicated platform to showcase these cutting-edge advancements. It features six rigorously peer-reviewed papers that present state-of-the-art research and applications in the rapidly evolving field of RAG.
Discipline
Artificial Intelligence and Robotics
Research Areas
Intelligent Systems and Optimization
Areas of Excellence
Digital transformation
Publication
Big Data Mining and Analytics
Volume
9
Issue
2
First Page
339
Last Page
340
ISSN
2096-0654
Identifier
10.26599/BDMA.2026.9020002
Publisher
Tsinghua University Press
Citation
YU, Philip S.; WANG, Haofen; and ZHU, Feida.
Editorial: Special section on challenges and opportunities in retrieval-augmented generation for LLMs: Techniques, trends, and applications. (2026). Big Data Mining and Analytics. 9, (2), 339-340.
Available at: https://ink.library.smu.edu.sg/sis_research/11061
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.26599/BDMA.2026.9020002