Editorial: Special Section on Challenges and Opportunities in Retrieval-Augmented Generation for LLMs: Techniques, Trends, and Applications
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.
Fields of Study & Subjects
Topic Modeling
Information Retrieval and Search Behavior
Multimodal Machine Learning Applications
Publication details
- Journal / source
- Big Data Mining and Analytics
- Volume
- 9
- Issue
- 2
- Pages
- 339–340
- Publication date
- 6 February 2026
- Language
- EN