About This Special Issue

Large Language Models (LLMs) have made remarkable strides in natural language processing, yet they continue to face significant challenges, including issues related to information accuracy, timeliness, and opaque reasoning processes. One promising approach to address these issues is Retrieval-Augmented Generation (RAG). By integrating knowledge from external databases, RAG enhances the accuracy and reliability of generated content, particularly for knowledge-intensive tasks. This methodology enables continuous knowledge updates and the incorporation of domain-specific information, effectively combining the inherent knowledge of LLMs with the vast resources of external databases.

This Special Issue aims to provide a platform for sharing cutting-edge research, practical experiences, and innovative solutions related to the integration of RAG and LLMs. By gathering high-quality contributions, we seek to facilitate knowledge exchange and promote the adoption of LLMs and RAG across diverse fields.

We welcome contributions focused on the following topics:

Innovative advancements and new architectures in RAG
Real-world case studies and deployment experiences of RAG on LLMs
Optimization techniques for enhanced performance and efficiency
Applications of RAG in low-resource settings and languages
Cross-disciplinary applications of LLMs
Security and privacy concerns related to RAG applications
Future trends and predictions in RAG development

We encourage researchers, practitioners, and experts from academia to participate and contribute to the advancement of the RAG and LLM fields. Submitted papers should reflect innovative research findings and practical insights, playing a crucial role in the evolution of this domain.

Submission Guidelines

Researchers and practitioners are invited to submit original research articles, letters, commentaries, reviews, and perspectives that address the aforementioned topics. All submissions will undergo a single-blind peer-review process with at least two review reports received to ensure the quality and relevance of the published work as the regular submission. Articles are published online immediately upon acceptance. The Article Processing Charge (APC) for publication is fully waived for this special issue.

Manuscripts should be prepared according to the journal's Instructions for Authors and submitted through the online submission system.

If you have any questions, please feel free to contact the editorial office or the guest editor.

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