## 1 Introduction  
Rapidly developing Large Language Models (LLMs) like GPT-4 [1] and LLaMA [2] have transformed natural language processing (NLP) and artificial intelligence (AI) by generating human-like text and interpreting complex linguistic nuances across a wide range of fields. For example, LLMs have demonstrated rational thinking capabilities spanning drug discovery to personalized learning. However, such models are limited by the static knowledge they acquire during pre-training; this can lead to inaccuracies (especially in rapidly evolving fields). Inaccuracies often include plausible sounding but factually incorrect responses or \"hallucinations\" [3, 4]. Moreover, these inaccuracies could be purposefully generated for malicious usage (e.g., promotion of certain drugs) by mixing authentic papers with fallacious papers to mislead the public [5].  
Retrieval-Augmented Generation (RAG) addresses these limitations by combining the generative power of LLMs with the precision of real-time information retrieval. RAG enhances LLM performance by grounding responses in dynamically updated and retrievable content to improve accuracy and reliability. This approach is crucial in fields like law, medicine, finance, and personalized care, which critically require accurate, up-to-date information. Additionally, RAG mitigates hallucinations by anchoring outputs in verifiable sources, enabling users to trace and validate the information provided [6, 7]. A systematic literature review (SLR) methodically identifies, evaluates, and synthesizes existing research on a specific topic. It typically involves several key processes: defining research questions, systematically searching for relevant studies, screening and selecting pertinent studies, extracting data, and analyzing and synthesizing subsequent findings. RAG-based LLMs can facilitate and potentially automate these tasks to significantly improve efficiency and accuracy. This study provides a comprehensive overview of the primary methodologies associated with using RAG in LLMs. Furthermore, it examines the application of RAG-based models to systematic literature reviews (SLRs), identifying existing gaps and emerging trends in this domain. Finally, this paper proposes a novel framework for automating systematic literature reviews through the application of RAG-based LLMs. Our proposed framework showcases a comprehensive solution for the automation of the four key stages of the SLR process, namely, literature search, literature screening, data extraction, and information synthesis. It enhances both the efficiency and accuracy at each stage to provide a more robust research compilation from relevant literature studies.  
This study therefore makes two significant contributions with the automation of systematic literature reviews:  
* The in-depth review of RAG-based LLMs and their applications to SLRs establishes a solid foundation for identifying existing research gaps and outlining future research directions.
* The proposed framework for RAG-based LLMs comprehensively addresses the entire SLR process, accommodating its iterative and incremental nature. This framework provides a robust starting point for enhancing and automating SLR tasks, thus contributing to the advancement of automation in this field.