用低成本大模型+高精度模型动态筛选文献，省时省力。
Leveraging LLMs for Title and Abstract Screening for Systematic Review: A Cost-Effective Dynamic Few-Shot Learning Approach
- 先用便宜模型初筛，再让高精度模型复核不确定项。
- 在10个真实系统综述中验证，效率提升显著且成本可控。
- 适合需要快速完成文献筛选的医学研究者和团队。
系统综述是循证医学的核心，对整合现有研究证据、指导临床决策至关重要。然而，随着科研论文数量激增，系统综述的执行变得日益繁重，其中标题与摘要筛选是最耗时、最耗资源的环节之一。为缓解这一问题，我们设计了一种两阶段动态少样本学习（DFSL）方法，旨在提升大语言模型（LLMs）在标题与摘要筛选任务中的效率与性能。该方法首先使用低成本的LLM进行初步筛选，随后对低置信度样本由高性能LLM重新评估，从而在控制计算成本的同时提升筛选效果。我们在10个真实系统综述上进行了评估，结果表明该方法具有强大的泛化能力与成本效益，有望显著减轻人工筛选负担，并加速系统综述的实际应用进程。
原文摘要 · Abstract (English)
Systematic reviews are a key component of evidence-based medicine, playing a critical role in synthesizing existing research evidence and guiding clinical decisions. However, with the rapid growth of research publications, conducting systematic reviews has become increasingly burdensome, with title and abstract screening being one of the most time-consuming and resource-intensive steps. To mitigate this issue, we designed a two-stage dynamic few-shot learning (DFSL) approach aimed at improving the efficiency and performance of large language models (LLMs) in the title and abstract screening task. Specifically, this approach first uses a low-cost LLM for initial screening, then re-evaluates low-confidence instances using a high-performance LLM, thereby enhancing screening performance while controlling computational costs. We evaluated this approach across 10 systematic reviews, and the results demonstrate its strong generalizability and cost-effectiveness, with potential to reduce manual screening burden and accelerate the systematic review process in practical applications.
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