Dense Retrieval Model

Dense retrieval models aim to efficiently find relevant information within massive datasets by representing text as dense vectors, enabling faster similarity comparisons than traditional methods. Current research focuses on improving model robustness, efficiency, and multilingual capabilities, exploring architectures like ColBERT and leveraging techniques such as knowledge distillation, instruction tuning, and contrastive learning to enhance retrieval accuracy. These advancements are significant for various applications, including improved search engines, question answering systems, and knowledge retrieval tasks, particularly in low-resource language settings.