What is Reranking
Reranking is a crucial refinement step in information retrieval systems that operates on an initial set of retrieved documents to produce a more accurate ordering based on relevance to a query. While traditional retrieval methods like BM25 or dense vector search efficiently narrow down large document collections to manageable candidate sets, they often lack the nuanced understanding required for optimal ranking. Rerankers address this limitation by employing more sophisticated, computationally intensive models that can assess query-document relevance with greater precision, typically by analyzing the full text of both query and document in tandem rather than relying solely on independent embeddings or lexical matching.
The architecture of modern rerankers typically involves cross-attention mechanisms that allow the model to directly compare query tokens with document tokens, creating a rich interaction matrix that captures semantic relationships at a granular level. This cross-encoder approach stands in contrast to bi-encoders used in initial retrieval, where queries and documents are encoded independently. By processing query-document pairs jointly, rerankers can identify subtle semantic alignments, handle complex multi-hop reasoning, and better understand contextual nuances that determine true relevance. The trade-off, however, is computational cost: while bi-encoders can pre-compute document embeddings once and reuse them, cross-encoders must process each query-document pair individually at inference time.
Several prominent reranking models have emerged as industry standards, each with distinct characteristics. Cohere's Rerank models, particularly Rerank-3, leverage large-scale training on diverse query-document pairs to achieve strong multilingual performance and handle longer contexts effectively. Cross-Encoder models from the sentence-transformers library, such as ms-marco-MiniLM-L-12-v2, offer open-source alternatives trained specifically on passage ranking tasks. BGE-reranker models from the Beijing Academy of Artificial Intelligence provide competitive performance with efficient inference characteristics. MonoT5 and related models adapt text-to-text transformers for relevance scoring by framing reranking as a sequence generation task, while models like RankLLaMA demonstrate how large language models can be fine-tuned specifically for ranking objectives.
The functional operation of a reranker involves several key phases: receiving a candidate set of documents from the initial retrieval stage, computing relevance scores for each query-document pair through forward passes of the cross-encoder model, and outputting a reordered list based on these scores. Many implementations incorporate strategies to manage computational constraints, such as processing documents in batches, implementing score caching for repeated queries, or using cascaded architectures where cheaper models filter candidates before expensive rerankers process the remaining documents. Advanced rerankers may also incorporate additional signals like recency, authority, or user context to produce more personalized rankings beyond pure semantic relevance.
The impact of reranking on retrieval quality can be substantial, with typical improvements of 10-30% in metrics like Mean Reciprocal Rank or Normalized Discounted Cumulative Gain when applied to output from first-stage retrievers. This performance gain is particularly pronounced in domains requiring deep semantic understanding, such as legal document search, medical literature retrieval, or technical support systems where distinguishing between superficially similar but fundamentally different documents is critical. As retrieval-augmented generation systems become more prevalent, reranking serves as an essential component in the pipeline, ensuring that language models receive the most relevant context for generating accurate, grounded responses rather than being misled by tangentially related documents that passed the initial retrieval threshold.