## 3 Applications of RAG-Based LLMs for Systematic Literature Reviews
### Applications of RAG-Based LLMs Adaptable for SLRs  
Despite little research specifically focused on RAG-based LLMs' applications to SLRs, their demonstrated capabilities with various NLP tasks, such as question answering (QA) (including open-domain, abstractive, and extractive QA) [9], fact verification [12], and text summarization [50], showcase their potential to assist with and potentially automate SLR tasks. We now review RAG-based LLMs' example applications in this context.  
RAG-based LLMs can extract data from scientific documents by employing QA techniques to identify and extricate key elements (e.g., experimental results, methodologies, and significant findings). For example, MEDRAG [51], a RAG toolkit specifically designed for medical QA, has markedly enhanced six different LLMs' performance: it elevated GPT-3.5 to GPT-4-level capabilities with up to 18% improvement across a variety of medical QA tasks. Similarly, Almanac [52]--a RAG-based model for clinical medicine--leverages web browsers to extract contextual articles before employing a dense retriever to select their most relevant text segments. It then utilizes GPT-3 to generate medical guidelines and treatment recommendations. An evaluation by a panel of physicians stated that these results outperformed those generated by GPT-3.  
Furthermore, RAG-based LLMs can produce concise summaries of individual research papers and collections of articles using text summarization techniques, thereby allowing researchers to swiftly grasp essential findings from extensive bodies of literature. For instance, SciDaSynth [53] uses a RAG-based GPT-4 model to retrieve relevant text snippets from scientific papers, subsequently generating data tables and summaries from the extracted information. Similarly, CCS Explorer [54]--a RAG-based framework automating clinical cohort studies--uses articles extracted from PubMed to perform sentence relevance prediction, extractive summarization, and detection of key entities (e.g., patients, outcomes, and interventions).  
For citation recommendations, RAG-based LLMs generate queries from specific pieces of text, retrieve relevant documents, and then use those documents to produce appropriate citations. For example, REASONS [55] (REtrieval and Automated citationS Of scieNtific Sentences) is a benchmark designed to evaluate LLMs' abilities to generate accurate citations based on context; RAG models tested on this benchmark outperformed ChatGPT-4. In addition, RAG-based LLMs can help identify research trends over time by analyzing and retrieving relevant literature, allowing researchers to track different fields' evolving interests and findings. A RAG-based LLMaA2 model, for instance, has been employed to identify emerging trends in various technologies (like cryptocurrency, reinforcement learning, and quantum machine learning) from extensive datasets of publications and patents; its predictions have been validated against the Gartner Hype Cycle [56].  
RAG-based LLMs have demonstrated considerable effectiveness in performing key tasks associated with SLRs, such as extracting critical data from scientific documents, generating concise summaries of individual papers and collections, providing accurate citation recommendations, and identifying emerging research trends. Illustrative examples, including MEDRAG, Almanac, SciDaSynth, CCS Explorer, and REASONS, highlight the versatility and capability of RAG-based models to effectively handle various SLR-related tasks. Therefore, the integration of RAG-based LLMs into the SLR process represents a promising avenue for advancing a more automated, efficient, and insightful approach to scholarly research synthesis and analysis.