Formality Transfer

Formality transfer, in its broadest sense, aims to leverage knowledge learned in one context (the "source") to improve performance in a related but different context (the "target"). Current research focuses on adapting this concept across diverse domains, employing techniques like transfer learning within neural networks (including transformers and convolutional neural networks), multi-armed bandit algorithms, and knowledge distillation. This research is significant because it addresses the challenge of data scarcity in many applications, improving efficiency and performance in areas such as financial prediction, robotic manipulation, and natural language processing.