Answers are grounded in this paper and cite their sources.
Evaluation demonstrates up to 82% accuracy for ChatGPT in screening articles across five systematic review datasets, highlighting the potential for automated literature selection.
Systematic reviews (SRs) provide valuable evidence for guiding new research directions. However, the manual effort involved in selecting articles for inclusion in an SR is error-prone and time-consuming. While screening articles has traditionally been considered challenging to automate, the advent of large language models offers new possibilities. In this paper, we discuss the effect of using ChatGPT on the SR process. In particular, we investigate the effectiveness of different prompt strategies for automating the article screening process using five real SR datasets. Our results show that ChatGPT can reach up to 82% accuracy. The best performing prompts specify exclusion criteria and avoid negative shots. However, prompts should be adapted to different corpus characteristics.
No takes yet. Share an insight, caveat, or question.
Syriani et al. (2024) studied this question.
Synapse has enriched 10 related papers. Consider them for comparative context: