How does a 'cross-encoder' specifically improve the relevance of retrieved documents in a RAG pipeline compared to initial ranking based solely on embedding similarity?

A cross-encoder is a neural network model, typically built on a transformer architecture, designed to directly assess the relevance between a query and a document. Unlike initial ranking methods based solely on embedding similarity, which use separate encoders to create independent vector representations (embeddings) for the query and each document, a cross-encoder takes the query and document concatenated together as a single input sequence. This fundamental difference allows the model's attention mechanism to perform deep, token-level interactions and learn fine-grained relationships *betweenthe query terms and the document terms. The output of a cross-encode....