Benefits of Transformer: In-Context Learning in Linear Regression Tasks with Unstructured Data

In practice, it is observed that transformer-based models can learn concepts in context in the inference stage. While existing literature, e.g., \citet{zhang2023trained,huang2023context}, provide theoretical explanations on this in-context learning ability, they assume the input $x_i$ and the output $y_i$ for each sample are embedded in the same token (i.e., structured data). However, in reality, they are presented in two tokens (i.e., unstructured data \cite{wibisono2023role}). In this case, this paper conducts experiments in linear regression tasks to study the benefits of …

benefits concepts context cs.cl cs.lg data embedded in-context learning inference learn linear linear regression literature practice regression sample stage stat.ml tasks token transformer unstructured unstructured data