# Leveraging LLMs for Efficient Topic Reviews
## 2 Literature Review  
In the context of ML applications in the literature review process, there is a growing integration of ML and text-mining tools to enhance the efficiency and efficacy of systematic reviews. In recent years, the authors emphasize advancements in query generation and refinement techniques. Ref. [20] discusses an automatic query generation approach using pre-trained language models, significantly surpassing traditional models in precision, recall, and F-measures, and sometimes even manual queries. In parallel, ref. [21] analyzes query logs from a specialized tool, revealing intuitive user behaviors in query formulation for medical systematic reviews. Similarly, another research trend illustrates the effectiveness of semi-automated text-mining tools in identifying relevant articles and developing efficient search strategies. Ref. [22] assesses the utility of such tools in a systematic review of diagnostic test accuracy, yielding additional relevant articles but with varied precision. Complementarily, in [23], the authors focus on using text-mining software for medical subject headings (MeSH) term identification, effectively adding new tools to the Systematic Review Toolbox and highlighting the strategy's efficiency. Moreover, ref. [24] examines the quality and nature of search methods in Campbell systematic reviews, emphasizing the importance of adhering to Methodological Expectations of Campbell Collaboration Intervention Reviews (MECCIR) and Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) [25] guidelines for comprehensive and reproducible searches. These studies collectively reflect a paradigm shift towards leveraging ML and text mining for optimizing literature review processes in systematic reviews. In recent years, Automated Text Classification (ATC) in systematic review has had a significant step toward more efficient, accurate, and less labor-intensive research methodologies. Traditional ML and zero-shot classification methods have shown promise, achieving competitive accuracy, precision, and recall across various datasets, thereby reducing human error and workload in abstract screening [3]. A paradigm shift towards semi-automated screening is observed, where decision support tools integrate multiple ML algorithms and feature selection techniques, demonstrating high effectiveness in areas like internet-based randomized controlled trials [4]. Hybrid feature selection rule measures (HFSRM) and logistic regression classifiers have been effectively applied in HIV literature and diagnostic test accuracy (DTA) reviews, significantly reducing screening time and effort [26; 27; 28]. Support vector machines (SVMs) and ensemble methods, trained interactively by reviewers, have been instrumental in distinguishing relevant from irrelevant citations, though class imbalance remains a challenge [29; 30]. The efficiency of ATC is further exemplified in food safety systematic reviews, where combinations of ML algorithms reduced the volume of articles for expert review while maintaining relevance [29]. Moreover, advancements in NLP have facilitated prioritizing title and abstract screening, significantly reducing workload and bias in literature reviews [31; 32]. In [33], the authors propose a ML-aided pipeline, ASReview, to significantly improve the efficiency and quality of systematic reviews and meta-analyses. This tool, designed to address the inefficiencies of manual screening, leverages active learning to systematically and accurately filter relevant studies from vast amounts of scientific literature. In terms of citation classification and research evaluation, several studies collectively contribute to a deeper understanding and advancement in citation classification and research evaluation, leveraging ML and NLP techniques for more efficient and comprehensive analysis of scholarly literature. Ref.