Prompts Generalize with Low Data: Non-vacuous Generalization Bounds for Optimizing Prompts with More Informative Priors
机构 * Google Deepmind(谷歌DeepMind)
Comments EXAIT Workshop paper at ICML 2025
期刊&会议
International Conference on Machine Learning · 会议 · Machine Learning
机构 * Google Deepmind(谷歌DeepMind)
Comments EXAIT Workshop paper at ICML 2025
机构 * Department of Computing and Software, McMaster University(计算与软件系,麦斯特大学;加拿大 CIFAR 人工智能主席,向量研究所) ; Canada CIFAR AI Chair, Vector Institute(计算机科学系,巴伊兰大学) ; Department of Computer Science, Bar-Ilan University(计算机科学系,贝纳-加隆大学) ; Department of Computer Science, Ben-Gurion University of the Negev
Comments Published at ICML 2025
Journal ref Proceedings of the 42nd International Conference on Machine Learning (ICML), PMLR 267:52508-52525, 2025
机构 * Microsoft(微软公司) ; School of Informatics, Xiamen University(厦门大学信息学院) ; Key Laboratory of Digital Protection(数字保护关键实验室) ; Taiwan (Xiamen University), Ministry of Culture(文化部(厦门大学)) ; Tsinghua University(清华大学)
Comments ICML 2025
机构 * Department of Computer Science, Dalhousie University, Halifax, Canada(计算机科学系,达尔豪斯大学,哈利法克斯,加拿大)
Journal ref Proceedings of the 42nd International Conference on Machine Learning, PMLR 267:51747-51769, 2025