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.
翻译：大语言模型（LLMs）在自然语言理解与生成、图像识别及多模态任务中展现出卓越能力，为通用人工智能（AGI）的发展指明了方向，并已成为全球科技竞争的核心议题。本文从用户视角系统综述了支撑LLMs的核心技术，包括提示工程、知识增强的检索增强生成、微调、预训练及工具学习。同时，回顾了科学学（SciSci）的历史发展，并对LLMs在科学计量学领域的潜在应用提出了前瞻性展望。此外，探讨了基于AI代理的科学评估模型前景，并介绍了利用LLMs进行新兴研究前沿探测与知识图谱构建的新方法。
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