Generalization Bounds for Transformers on Variable-Size Inputs

This ArXiv paper likely explores the theoretical underpinnings of Transformer performance, specifically focusing on how they generalize when processing inputs of different sizes. Understanding these bounds is crucial for improving model training and deployment.

• •Focuses on how Transformers generalize on variable-size inputs.
• •Investigates theoretical limitations of Transformer performance.
• •Potentially provides insights for model training optimization.