Knowledge Augmentation for Language Models

Modern language models have the capacity to store and use immense amounts of knowledge about real world. Yet, their knowledge about the world is often incorrect or outdated, motivating ways to augment their knowledge. In this talk, I will present two complementary avenues for knowledge augmentation: (1) a modular, retrieval-based approach which brings in new information at inference time and (2) a parameter updating approach which aims to enable models to internalize new information and make inferences based on it.