What is Re-Ranking?
Re-ranking is a two-stage retrieval approach where initial results from a fast but approximate retrieval method are reordered using a more sophisticated but computationally expensive scoring model. The first stage uses efficient methods like embedding similarity or BM25 to quickly retrieve a candidate set (often 100-1000 documents) from a large corpus. The second stage applies a more accurate model to this smaller set, producing a refined ranking that better reflects true relevance to the query.
Re-ranking models are typically cross-encoders that process the query and document together, allowing them to capture complex interactions between query terms and document content that bi-encoder approaches miss. These models achieve higher accuracy but are too slow to apply to entire document collections. By applying them only to top candidates from the first stage, re-ranking balances accuracy and efficiency. Popular re-ranking models include specialized transformers trained on relevance datasets like MS MARCO.
Re-ranking has become a best practice in production retrieval systems, consistently improving results with minimal architecture changes. The quality improvement can be substantial, particularly for queries where simple semantic similarity doesn't capture all relevance factors. The technique integrates well with other retrieval improvements: initial retrieval might use hybrid search, and re-ranking further refines those results. Many vector databases and RAG frameworks now include built-in re-ranking support, making it easy to add this optimization to existing systems.