Neural Ranker

Neural rankers are machine learning models designed to order items (e.g., search results, recommendations) based on relevance, aiming to optimize metrics like precision and NDCG. Current research focuses on improving robustness against adversarial attacks, enhancing efficiency through techniques like prompt-based learning and decoder-only architectures, and adapting to limited labeled data via unsupervised methods and active learning strategies. These advancements are significant for various applications, including information retrieval, recommendation systems, and even medical screening, where efficient and reliable ranking is crucial for decision-making.