Zero Shot Dense Retrieval

Zero-shot dense retrieval aims to build information retrieval systems that can effectively search new, unseen datasets without any task-specific training data. Current research focuses on improving the generalization ability of these systems through techniques like unsupervised pre-training with instruction tuning, iterative distillation between retrievers and rerankers, and contrastive learning to handle distribution shifts between training and target data. These advancements are significant because they address the high cost and difficulty of obtaining labeled data for training traditional dense retrieval models, paving the way for more robust and adaptable information retrieval systems across diverse domains and languages.