## 6 Conclusions  
The Retrieval-Augmented Generation (RAG) framework comprises three fundamental processes: retrieval, augmentation, and generation. The effectiveness of the retrieval process is contingent upon various factors, including the type of retriever employed, the choice of information sources, the granularity of retrieval, and any enhancements applied either before or after retrieval. Generation can employ either Open-source Large Language Models (LLMs) that provide flexibility and allow for task-specific customization or closed-source LLMs that are often preferred for their advanced capabilities and the availability of commercial support. For augmentation, the iterative and adaptive nature of the retrieval process, characterized by multiple cycles, incrementally refines outputs by incorporating more contextually relevant data.  
RAG-based LLMs have significantly improved accuracy, relevance, and contextual comprehension by combining real-time information retrieval with generative functions. These enhancements position RAG-based LLMs as promising candidates for automating various tasks associated with systematic literature reviews. Automated tasks, such as data extraction, summarization, research synthesis, and trend identification, have the potential to inform the scientific community about emerging fields of study more effectively. However, this application area is still under-explored within the research community. Critical areas that warrant further investigation include the integration of domain-specific LLMs, the processing and generation of multimodal data, and the utilization of multiple retrieval sources.  
To address these needs, a framework employing RAG-based LLMs for SLRs is proposed. Our framework encompasses the four stages of the SLR process: literature search, literature screening, data extraction, and information synthesis. Future research should aim to optimize the synergy between LLM selection, training strategies, RAG techniques, and prompt engineering to implement the proposed framework, with a particular focus on the retrieval of information from individual scientific papers and the integration of retrieved data to generate outputs on diverse aspects such as current status, existing gaps, and emerging trends.  
**Author Contributions:** Conceptualization, B.H. and T.S.; methodology, B.H.; software, B.H.; validation, B.H., T.S. and A.M.; formal analysis, B.H.; investigation, B.H.; resources, B.H.; data curation, B.H.; writing--original draft preparation, B.H.; writing--review and editing, B.H., T.S. and A.M.; visualization, B.H.; supervision, T.S. and A.M.; project administration, B.H. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data Availability Statement: Data is contained within the article.  
This manuscript employed OpenAI's GPT-4 to strengthen the sentence structure and accelerate the writing speed. All intellectual property (IP) in this document is exclusively owned by the authors. GPT-4 has not contributed to any of the original ideas, insights, or intellectual content presented in this manuscript.  
The authors declare no conflicts of interest.