Diversity Enhancement

Diversity enhancement in various machine learning contexts aims to improve model performance and robustness by generating more varied and representative outputs or datasets. Current research focuses on incorporating diversity measures into model training and data selection, employing techniques like contrastive learning, multi-armed bandits, and evolutionary algorithms to achieve this goal across diverse applications such as large language models, crowd simulation, and federated learning. These advancements are significant because they address limitations of existing methods that often lead to overfitting, biased results, or a lack of generalizability, ultimately improving the reliability and fairness of AI systems.