CAREER: Theory and Algorithms for Learning with Frozen Pretrained Models

使预训练模型适应新任务和新数据对于现代机器学习的民主化至关重要。由于原始训练数据的巨大规模和不可访问性，不可能对模型进行完全的再训练，因此将大型预训练模型应用于定制任务或新数据是困难的。训练数据的不可访问性和大容量也使得传统的领域自适应技术难以实现。为了克服这一挑战，已经转向模块化适应方法，使“冻结”的预训练模型能够在最小或不修改其内部参数的情况下进行微调。尽管这些方法在一些实际应用中取得了成功，但对影响预训练模型微调效果的因素的理论认识还存在空白。该项目开发了一个系统框架，将奠定理论基础，并导致严格的微调原则。该项目有可能影响依赖于预训练机器学习模型适应的广泛科学领域和行业。该项目更广泛的影响包括建立推进STEM的教育计划，为现代机器学习领域未来劳动力的发展做出贡献。该项目的目标是建立一个统一的理论，并为使用冻结预训练模型的新兴学习范式设计具有可证明保证的新算法。本文将建立一个数学框架，以促进在各种适应和微调方法下冻结预训练模型的表达能力的理论分析。该项目将研究三种适应策略：(1)参数高效微调，在保持其余参数不变的同时，更新最小部分预训练模型的参数；(2)输入/输出处理，即对预训练模型的输入或输出进行修改；(3)模型组合，构建一个由多个预训练模型组成的系统，以解决更复杂的任务。除了分析这些方法外，该项目还旨在开发具有可证明性能保证的新型自适应算法。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Adapting pretrained models to new tasks and new data is crucial for the democratization of modern machine learning. Application of large pretrained models to customized tasks or new data is difficult since full retraining of the model is not possible due to the huge size and inaccesibility of the original training data. The inaccessibility and large size of the training data also renders traditional domain adaptation techniques impractical. To overcome this challenge there has been shift towards modular adaptation methods that enable the fine-tuning of "frozen" pretrained models with minimal or no modifications to their internal parameters. Despite the success of these methods in some practical applications, there is a gap in theoretical understanding of the factors affecting the effectiveness of finetuning of pretrained models. This project develops a systematic framework that will lay theoretical foundations and lead to rigorous fine-tuning principles. The project has the potential to impact a wide range of scientific fields and industries that rely on adaptation of pretrained machine learning models. The project's broader impact includes the establishment of educational initiatives for advancing STEM, contributing to the development of the future workforce in modern machine learning.The project's goal is to establish a unified theory and devise new algorithms with provable guarantees for the emerging paradigm of learning with frozen pretrained models. A mathematical framework will be developed to facilitate the theoretical analysis of the expressive power of frozen pretrained models under various adaptation and fine-tuning methods. The project will investigate three adaptation strategies: (1) parameter-efficient fine-tuning, which updates a minimal portion of the pretrained model's parameters while keeping the rest unchanged; (2) input/output processing, which involves modifying the input entering or the output produced by the pretrained models; and (3) model composition, which constructs a system of multiple pretrained models to address more complex tasks. In addition to analyzing these methods, the project aims to develop novel adaptation algorithms with provable performance guarantees.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.