Retrieval Model

Retrieval models aim to efficiently select relevant information from large datasets in response to a query, serving as a crucial component in various applications like question answering and recommendation systems. Current research emphasizes improving retrieval accuracy and robustness through techniques like instruction-tuning, the use of multiple expert models with routing mechanisms, and hybrid approaches combining different retrieval methods. These advancements are driving significant improvements in downstream tasks, impacting fields ranging from legal information retrieval to personalized language learning and enhancing the efficiency and effectiveness of large language models.