Neural Retrieval

Neural retrieval aims to improve information retrieval by leveraging deep learning models to represent and compare text data, enabling more semantically relevant search results. Current research focuses on enhancing model robustness against adversarial attacks and out-of-distribution data, improving efficiency through techniques like GPU acceleration and optimized indexing structures (e.g., tree-based indexes), and addressing biases introduced by the increasing prevalence of AI-generated content. These advancements are significant for improving the accuracy and fairness of search engines and recommendation systems across various applications, including web search, e-commerce, and question answering.