Knowledge Augmentation

Knowledge augmentation enhances large language models (LLMs) and other machine learning models by incorporating external knowledge sources to improve performance and address limitations like outdated information or data scarcity. Current research focuses on integrating diverse knowledge types—including textual definitions, visual data, numerical parameters, and structured knowledge graphs—using various techniques such as prompt engineering, knowledge injection, and data augmentation strategies tailored to specific tasks (e.g., question answering, image recognition, and moral event extraction). This work is significant because it improves model accuracy and efficiency, particularly in low-data regimes, and enables applications in diverse fields like healthcare, education, and misinformation detection.