Systematic reviews are comprehensive reviews of the literature for a highly focused research question. These reviews are often treated as the highest form of evidence in evidence-based medicine, and are the key strategy to answer research questions in the medical field. To create a high-quality systematic review, complex Boolean queries are often constructed to retrieve studies for the review topic. However, it often takes a long time for systematic review researchers to construct a high quality systematic review Boolean query, and often the resulting queries are far from effective. Poor queries may lead to biased or invalid reviews, because they missed to retrieve key evidence, or to extensive increase in review costs, because they retrieved too many irrelevant studies. Recent advances in Transformer-based generative models have shown great potential to effectively follow instructions from users and generate answers based on the instructions being made. In this paper, we investigate the effectiveness of the latest of such models, ChatGPT, in generating effective Boolean queries for systematic review literature search. Through a number of extensive experiments on standard test collections for the task, we find that ChatGPT is capable of generating queries that lead to high search precision, although trading-off this for recall. Overall, our study demonstrates the potential of ChatGPT in generating effective Boolean queries for systematic review literature search. The ability of ChatGPT to follow complex instructions and generate queries with high precision makes it a valuable tool for researchers conducting systematic reviews, particularly for rapid reviews where time is a constraint and often trading-off higher precision for lower recall is acceptable.

翻译：系统综述是针对高度聚焦的研究问题对文献进行的全面回顾。这类综述常被视为循证医学中的最高级别证据，也是回答医学领域研究问题的关键策略。为生成高质量系统综述，研究者常需构建复杂的布尔查询以检索相关研究。然而，系统综述研究者通常需要耗费大量时间才能构建出高质量的系统综述布尔查询，且最终查询往往效果欠佳。低效的查询可能导致偏倚或无效的综述（因遗漏关键证据），或大幅增加综述成本（因检索到过多不相关研究）。基于Transformer的生成模型近期取得了显著进展，展现出遵循用户指令并生成相应答案的强大潜力。本文通过标准测试集上的大量实验，研究了最新此类模型ChatGPT在系统综述文献检索中生成有效布尔查询的能力。结果发现：ChatGPT能够生成高检索精度的查询，但会以召回率为代价。总体而言，本研究证明了ChatGPT在生成系统综述文献检索有效布尔查询方面的潜力。其遵循复杂指令并生成高精度查询的能力，使其成为系统综述研究者（尤其是时间受限、可接受以更高精度换取更低召回率的快速综述）的重要工具。