Auxiliary Knowledge

Auxiliary knowledge, encompassing diverse information sources beyond primary data, is increasingly used to improve machine learning model performance and robustness. Current research focuses on integrating auxiliary knowledge in various forms, such as semantic relations, commonsense reasoning, and even data from unrelated tasks, employing techniques ranging from graph-based methods to novel semi-supervised algorithms and prompt engineering with large language models. This approach demonstrates significant improvements across diverse applications, including distracted driver detection, public health intervention evaluation, and named entity recognition, highlighting the potential for enhancing model accuracy, generalizability, and explainability.