Systematic reviews (SRs) - the librarian-assisted literature survey of scholarly articles takes time and requires significant human resources. Given the ever-increasing volume of published studies, applying existing computing and informatics technology can decrease this time and resource burden. Due to the revolutionary advances in (1) Generative AI such as ChatGPT, and (2) External knowledge-augmented information extraction efforts such as Retrieval-Augmented Generation, In this work, we explore the use of techniques from (1) and (2) for SR. We demonstrate a system that takes user queries, performs query expansion to obtain enriched context (includes additional terms and definitions by querying language models and knowledge graphs), and uses this context to search for articles on scholarly databases to retrieve articles. We perform qualitative evaluations of our system through comparison against sentinel (ground truth) articles provided by an in-house librarian. The demo can be found at: https://youtu.be/zMdP56GJ9mU.

翻译：系统性综述（SR）——一种由图书馆员协助的学术论文文献综述方法，耗时且需要大量人力资源。鉴于已发表研究的数量持续增长，应用现有计算与信息技术可减轻此类时间和资源负担。受（1）生成式人工智能（如ChatGPT）以及（2）外部知识增强型信息提取方法（如检索增强生成）的革新性进展启发，本研究探索了将（1）和（2）中的技术应用于系统性综述的方法。我们展示了一个系统：该系统接收用户查询，通过查询扩展获取增强语境（包括通过查询语言模型和知识图谱获得的额外术语与定义），并利用此语境在学术数据库中搜索论文以获取文献。我们通过与内部图书馆员提供的标注文章（金标准）进行对比，对系统开展了定性评估。演示视频可见于：https://youtu.be/zMdP56GJ9mU。