Data-distributional Approaches for Generalizable Language Models

Explore data-distributional approaches for creating more generalizable language models in this comprehensive lecture by Stanford PhD student Sang Michael Xie. Discover principled methods to improve and understand language models by focusing on pre-training data distribution. Learn about optimizing data source mixtures for efficient multipurpose language model training, employing importance resampling to select relevant data from large-scale web datasets for specialized model training, and gain insights into the theoretical analysis of in-context learning. Understand how these approaches can enhance the capabilities and training efficiency of large language models, and how they relate to modeling coherence structure in pre-training data.