大语言模型赋能科学科学：新工具与新方法
摘要
Large language models (LLMs) have exhibited exceptional capabilities in natural language understanding and generation, image recognition, and multimodal tasks, charting a course towards AGI and emerging as a central issue in the global technological race. This manuscript conducts a comprehensive review of the core technologies that support LLMs from a user standpoint, including prompt engineering, knowledge-enhanced retrieval augmented generation, fine tuning, pretraining, and tool learning. Additionally, it traces the historical development of Science of Science (SciSci) and presents a forward looking perspective on the potential applications of LLMs within the scientometric domain. Furthermore, it discusses the prospect of an AI agent based model for scientific evaluation, and presents new research fronts detection and knowledge graph building methods with LLMs.
引用
@article{arxiv.2511.15370,
title = {The Empowerment of Science of Science by Large Language Models: New Tools and Methods},
author = {Guoqiang Liang and Jingqian Gong and Mengxuan Li and Gege Lin and Shuo Zhang},
journal= {arXiv preprint arXiv:2511.15370},
year = {2025}
}
备注
The manuscript is currently ongoing the underreview process of the journal of information science