探索大模型在高能物理中的应用前景，推动领域专用模型发展。
Foundation models for high-energy physics
- 首次系统综述高能物理领域基础模型的研究进展
- 分析大模型直接应用于粒子物理数据的可行性与挑战
- 适合关注AI与高能物理交叉研究的学者参考
基础模型——即大规模预训练机器学习模型，可微调用于多种任务——已彻底改变自然语言处理和计算机视觉领域。在高能物理领域，这些模型是否可以直接应用于物理研究，或从头构建专用于粒子物理数据的模型，正引发越来越多的关注。本文作为该领域首篇综述，总结并讨论了迄今已发表的相关研究工作。
原文摘要 · Abstract (English)
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.
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