The rise of foundation models -- large, pretrained machine learning models that can be finetuned to a variety of tasks -- has revolutionized the fields of natural language processing and computer vision. In high-energy physics, the question of whether these models can be implemented directly in physics research, or even built from scratch, tailored for particle physics data, has generated an increasing amount of attention. This review, which is the first on the topic of foundation models in high-energy physics, summarizes and discusses the research that has been published in the field so far.

翻译：基础模型——即经过预训练、可微调至多种任务的大型机器学习模型——的兴起，已在自然语言处理和计算机视觉领域引发革命。在高能物理中，这些模型能否直接应用于物理研究，甚至能否针对粒子物理数据从头构建专用模型，正日益受到广泛关注。本文作为高能物理领域首篇关于基础模型的综述，系统总结并讨论了该领域迄今已发表的研究成果。