Can ChatGPT Write a Good Boolean Query for Systematic Review Literature Search?
Shuai Wang, Harrisen Scells, Bevan Koopman, Guido Zuccon

TL;DR
This study evaluates ChatGPT's ability to generate Boolean queries for systematic reviews, finding it produces high-precision queries that can save time but may sacrifice recall, aiding rapid review processes.
Contribution
It demonstrates the potential of ChatGPT to generate effective Boolean search queries for systematic reviews, highlighting its usefulness in medical literature searches.
Findings
ChatGPT generates high-precision Boolean queries.
Trade-off observed between precision and recall.
Potential for rapid systematic review support.
Abstract
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…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsExplainable Artificial Intelligence (XAI) · Scientific Computing and Data Management · Data Stream Mining Techniques
MethodsTest
