Diversity Score

Diversity scores quantify the heterogeneity within datasets or model outputs, aiming to improve performance and reliability in various machine learning applications. Current research focuses on developing robust and efficient methods for calculating these scores across diverse data types, including images, text, and model behaviors, often employing techniques like entropy measures, compression algorithms, and human feedback integration. These scores are increasingly important for evaluating model generalization, identifying biases, and optimizing training data selection, ultimately leading to more robust and reliable AI systems.