Diversity Metric

Diversity metrics quantify the variety within a dataset or the outputs of a model, aiming to improve the representativeness and robustness of systems. Current research focuses on developing new metrics tailored to specific applications (e.g., text generation, search results, image synthesis), often incorporating similarity measures and leveraging techniques like determinantal point processes or multi-agent reinforcement learning for optimization. These advancements are crucial for enhancing the quality and reliability of machine learning models and improving the fairness and inclusivity of data-driven systems across various fields.