用大模型自动找论文，省去手动查文献的麻烦。
Scientific Knowledge Discovery in the Age of Large Language Models
- 用大模型替代人工写检索词，自动筛选文献。
- 涵盖34篇论文，验证了大模型在文献筛选中的有效性。
- 适合科研人员快速追踪领域进展。
学术文献的快速增长使识别相关出版物变得愈发困难，传统搜索系统仍严重依赖手工构建查询和费力的人工审查。生成式大语言模型（LLMs）提供了一种更灵活的替代方案，支持文献检索及根据纳入标准筛选候选研究。本章综述了通过在OpenAIRE图谱上进行布尔搜索（从1,589条记录中筛选出34篇纳入文献）识别出的34篇同行评审论文，这些研究涉及大模型的应用、模型访问与适配方式、提示工程与架构技术、真实标注来源及评估指标。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。