Differentiable Search

Differentiable search aims to integrate the indexing and retrieval stages of information retrieval into a single, differentiable neural network, eliminating the need for separate index structures. Current research focuses on improving the efficiency and scalability of these models, particularly addressing challenges like incremental updates to large corpora and optimizing for various hardware constraints, often employing transformer-based architectures and techniques like prompt engineering or quantization. This approach promises to simplify information retrieval systems, enabling end-to-end optimization and potentially leading to more efficient and effective search across diverse data types, including text and images.