Model Distribution

Model distribution research focuses on understanding and manipulating the probability distributions of model parameters and outputs, aiming to improve model performance, robustness, and interpretability. Current efforts involve developing novel training methods like those combining individually trained models or employing maximum entropy principles to enhance calibration and handle out-of-distribution data. These advancements are crucial for building more reliable and efficient large language models and generative models, particularly in high-dimensional spaces where traditional metrics can be misleading. Improved model distributions ultimately lead to better generalization, reduced vulnerability to adversarial attacks, and enhanced understanding of complex models.