Document Embeddings

Document embeddings represent text documents as dense vectors, aiming to capture semantic meaning and relationships for tasks like information retrieval and recommendation systems. Current research focuses on improving embedding quality through contextualized approaches (considering surrounding documents), efficient architectures like dual encoders with learnable late interactions, and methods to mitigate biases and improve zero-shot performance. These advancements are crucial for enhancing the efficiency and accuracy of various NLP applications, particularly in large-scale settings where speed and scalability are paramount.