Retrieve and Rerank

Retrieve-and-rerank is a two-stage information retrieval approach that first retrieves a broad set of candidate items using a fast, scalable method (e.g., bi-encoders, BM25), followed by a more accurate but computationally expensive reranking step (e.g., cross-encoders, transformers) to refine the initial results. Current research focuses on improving both retrieval and reranking stages, exploring techniques like LLM integration for iterative feedback, joint comparison of multiple candidates, and adaptive methods to minimize approximation errors. This framework is proving highly effective across diverse applications, including medical coding, multimedia retrieval, and cross-lingual information retrieval, demonstrating significant improvements in accuracy and efficiency compared to single-stage approaches.