Rerankers

A reranker receives a query and many documents, and returns a ranked list of relevancy between the query and documents. The documents are often the preliminary results from an embedding-based retrieval system, and the reranker refines the ranks of these candidate documents and provides more accurate relevancy scores.

Unlike embedding models that encode queries and documents separately, rerankers are cross-encoders that jointly process a pair of query and document, enabling more accurate relevancy prediction. Apply a reranker on the top candidates retrieved with embedding-based search or with lexical search algorithms such as BM25 and TF-IDF.