Retrieval Enhanced

Retrieval-enhanced methods aim to improve the performance of various machine learning models by incorporating information retrieved from external knowledge bases or similar data points. Current research focuses on integrating retrieval mechanisms into diverse architectures, including generative adversarial networks (GANs), graph neural networks (GNNs), and large language models (LLMs), often employing techniques like contrastive learning and knowledge distillation to optimize the retrieval and integration processes. This approach shows promise across numerous applications, from improving click-through rate prediction and question answering to enhancing image classification and biomedical entity linking, by addressing limitations of existing models in handling noisy data, rare entities, and complex reasoning tasks.