Polynomial Regression as a Task for Understanding In-context Learning Through Finetuning and Alignment

Max Wilcoxson, Morten Svendgård, Ria Doshi, Dylan Davis, Reya Vir, Anant Sahai

Abstract: Simple function classes have emerged as toy problems to better understand in-context-learning in transformer-based architectures used for large language models. But previously proposed simple function classes like linear regression or multi-layer-perceptrons lack the structure required to explore things like prompting and alignment within models capable of in-context-learning. We propose univariate polynomial regression as a function class that is just rich enough to study prompting and alignment, while allowing us to visualize and understand what is going on clearly.