The Empowerment of Science of Science by Large Language Models: New Tools and Methods

• URL: http://arxiv.org/abs/2511.15370v1
• Date: Wed, 19 Nov 2025 11:57:22 GMT
• Title: The Empowerment of Science of Science by Large Language Models: New Tools and Methods
• Authors: Guoqiang Liang, Jingqian Gong, Mengxuan Li, Gege Lin, Shuo Zhang,
• Abstract summary: Large language models (LLMs) have exhibited exceptional capabilities in natural language understanding and generation, image recognition, and multimodal tasks.<n>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.
• Score: 7.392576669260369
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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