大模型赋能科学学研究，带来新工具与方法。
The Empowerment of Science of Science by Large Language Models: New Tools and Methods
- 从用户视角梳理大模型核心技术，包括提示工程与知识增强生成。
- 提出基于大模型的科研前沿探测与知识图谱构建新方法。
- 展望智能代理在科学评估中的应用，适合科研管理者与政策制定者。
大型语言模型（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，正推动通用人工智能（AGI）发展，并成为全球科技竞争的核心议题。本文从用户角度全面回顾支撑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、预训练与工具学习。同时，梳理了科学学（SciSci）的发展历程，并展望大模型在科学计量领域的潜在应用。此外，文章探讨了基于AI代理的科学评价模型前景，提出了利用大模型实现科研前沿探测与知识图谱构建的新方法。
原文摘要 · Abstract (English)
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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