The empowerment of Science of Science by large language models: New tools and methods

Abstract

Large language models (LLMs) have exhibited exceptional capabilities in natural language understanding and generation, image recognition, and multimodal tasks, charting a course toward artificial general intelligence and emerging as a central issue in the global technological race. This article conducts a comprehensive review of the core technologies that support LLMs from a user’s standpoint, including prompt engineering, knowledge-enhanced retrieval-augmented generation (RAG), fine-tuning, pre-training, and tool learning. In addition, 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 in detection and knowledge graph building methods with LLMs.