Exploring the Interplay of Pretraining, Architecture, and Adaptation in Foundation Models
Mashrin Srivastava · 2023
Foundation models have emerged as powerful tools for learning and adapting to a wide range of tasks due to their ability to leverage large and diverse data sources. This paper aims to provide a comprehensive analysis of the key components that contribute to the success of foundation models, including pretraining, architecture, and adaptation mechanisms. We first investigate the role of pretraining in learning useful representations and how different objectives transfer to downstream tasks. We then delve into the impact of architectural choices on the learned representations, such as the effect of model scale, attention mechanisms, and nonparametric models. Finally, we explore different adaptation methods, including fine-tuning, few-shot learning, and prompting, to understand their efficiencies and limitations. Furthermore, we discuss the importance of robustness, calibration, and addressing biases in foundation models. We conclude by examining emergent phenomena and scaling laws in these models as they scale in size and capacity.