Scientific Information Research
Keywords
large language model; scientific and technical intelligence; intelligence method; intelligence practice; deeplearning; textual information
Abstract
ꎺ[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, intelligence evaluation, and intelligence prediction, resulting in significant optimization improvements or paradigm shifts.
Digital Object Identifier (DOI)
10.19809/j.cnki.kjqbyj.2025.01.005
Recommended Citation
HUA, Bolin and WANG, Yingze
(2025)
"Application of Large Language Model Methods in Scientific and Technical Intelligence Practice,"
Scientific Information Research: Vol. 7:
Iss.
1, Article 5.
DOI: 10.19809/j.cnki.kjqbyj.2025.01.005
Available at:
https://eng.kjqbyj.com/journal/vol7/iss1/5