科技情报研究2025，Vol.7Issue(1)：53-64,12.DOI:10.19809/j.cnki.kjqbyj.2025.01.005
大语言模型方法在情报实践中的应用
Application of Large Language Model Methods in Scientific and Tech-nical Intelligence Practice
摘要
Abstract
[Purpose/significance]With the strong ability to process large-scale datasets and outstanding performance in various natural language processing tasks,large language models(LLMs)have excelled across multiple industries.Since scientific and technical intelligence primarily relies on textual data,LLMs are naturally well-suited for this field,ushering in a new wave of transformative changes.[Method/process]This article discusses the advantages of LLMs from five perspectives:low-dimensional dense vector representations of text,large-scale pre-trained models,fine-tuning and prompt learning,high-quality large-scale training data,and human alignment techniques.[Result/conclusion]LLMs have extensive applications in tasks such as intelligence identification,intelligence tracking,intelli-gence evaluation,and intelligence prediction,resulting in significant optimization improvements or paradigm shifts.关键词
大语言模型/情报学/情报方法/情报实践/深度学习/文本信息Key words
large language model/scientific and technical intelligence/intelligence method/intelligence practice/deep learning/textual information引用本文复制引用
化柏林,王英泽..大语言模型方法在情报实践中的应用[J].科技情报研究,2025,7(1):53-64,12.基金项目
国家社会科学基金重大项目"大数据驱动的科技文献语义评价体系研究"(编号:21&ZD329) （编号:21&ZD329）
