Because foundation models are used to build many other models that are trained to new, more specific tasks, it can be hard to evaluate models consistently. The one-model-many-models paradigm attempts to study interpretability of foundation models by looking for similarities and differences across the foundation model and its downstream models to try and understand which behaviors were likely emergent from the foundation model itself, and which come from the derivative models.

Today I read about the five stages of foundation model development. The paper breaks foundation models down into these stages in order to specify the unique challenges and ethical considerations at each step of the process. The five stages are: data creation, data curation, training, adaptation, and deployment. Having this vocabulary for explaining the process of building AI models is a helpful way to emphasize the different challenges that face builders at each step.