Ranking

2 articles

Artificial Intelligence 13 Sep 2026 7 min read

Merge Retrieval Rankings with Reciprocal Rank Fusion

A lexical retriever and an embedding retriever can return useful results for the same query while assigning scores that have no common numerical meaning. Adding those raw scores treats incomparable scales as if they were calibrated measurements. Reciprocal rank fusion avoids that assumption by combining positions rather than score magnitudes. This makes RRF useful in retrieval-augmented generation systems that mix distinct retrieval signals. Each retriever keeps its own scoring model. The fusion layer only needs ordered result lists and stable document identities.

Artificial Intelligence Retrieval

Artificial Intelligence 13 Sep 2026 7 min read

Diversify Retrieval Results with Maximum Marginal Relevance

A retriever can fill its top positions with passages that are individually relevant but nearly interchangeable. Several chunks from one document may repeat the same fact, leaving little room for other evidence in a fixed context budget. Maximum marginal relevance, commonly abbreviated MMR, addresses this at the selection stage by considering both query relevance and redundancy with items already chosen. MMR does not change the embedding model or recover candidates that retrieval missed. It reranks a candidate pool. That boundary matters: the method can improve variety among available candidates, but it cannot compensate for poor candidate recall.

Artificial Intelligence Retrieval