Dense Retriever

Dense retrieval focuses on efficiently finding relevant information (e.g., documents, passages) from large datasets by representing both queries and data points as dense vectors, enabling fast similarity comparisons. Current research emphasizes improving the accuracy and efficiency of these methods, exploring techniques like contrastive learning, knowledge distillation, and the integration of large language models (LLMs) to enhance retrieval performance, particularly in low-resource or zero-shot scenarios. These advancements are significant for various applications, including question answering, conversational search, and biomedical literature search, by enabling faster and more accurate information access.