Cross Encoder

Cross-encoders are neural network architectures that jointly encode query and document pairs to determine semantic similarity, offering superior accuracy to methods that encode them separately (dual-encoders). Current research focuses on improving their efficiency for large-scale applications, including exploring shallow architectures, knowledge distillation from cross-encoders to more efficient dual-encoders, and adaptive indexing techniques to speed up k-NN search. These advancements are crucial for various applications like semantic search, question answering, and information retrieval, where high accuracy and scalability are essential.