发表机构
IMSI; ATHENA RC（IMSI; ATHENA研究中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本章综述34篇应用生成式大语言模型的同行评审论文，针对学术文献检索与合格研究筛选任务，基于OpenAIRE Graph布尔搜索筛选文献，为科学知识发现提供更灵活方案。
AI 中文摘要
学术文献的快速增长使得识别相关出版物愈发困难，而传统搜索系统仍严重依赖人工构建的查询与费力的人工检查。生成式大语言模型（LLMs）提供了更灵活的替代方案，支持文献检索及针对合格标准的候选研究筛选。本章对34篇经同行评审的论文进行了综述，这些论文将生成式LLMs应用于上述两项任务，通过对OpenAIRE Graph进行布尔搜索确定（筛选1589条记录后纳入34篇）。被综述的研究按所采用的LLMs、模型访问与适配、提示与架构技术、基准来源及评估指标进行了特征刻画。
英文摘要
The rapid growth of scholarly literature has made identifying relevant publications increasingly difficult, and conventional search systems still depend heavily on manually formulated queries and effortful manual inspection. Generative large language models (LLMs) offer a more flexible alternative, supporting literature retrieval and the screening of candidate studies against eligibility criteria. This chapter surveys 34 peer-reviewed papers applying generative LLMs to these two tasks, identified via a Boolean search over the OpenAIRE Graph (1,589 records screened to 34 inclusions). Reviewed studies are characterised by LLMs employed, model access and adaptation, prompting and architectural techniques, ground-truth sources, and evaluation metrics.
Comments21 pages, 4 figures
