Dimension Importance Estimation (DIME) is a framework designed to enhance dense information retrieval by identifying and pruning irrelevant dimensions from query embeddings. The article discusses various DIME approaches, including Magnitude DIME and Pseudo-Relevance Feedback DIME, which utilize different methods to assess the importance of dimensions and improve retrieval accuracy without requiring retraining or reindexing.

MUVERA is a novel retrieval algorithm that transforms complex multi-vector retrieval tasks into simpler single-vector maximum inner product searches, significantly improving efficiency without sacrificing accuracy. By utilizing fixed dimensional encodings (FDEs), MUVERA allows for rapid retrieval of relevant documents while leveraging existing optimized search techniques. Experimental results demonstrate its superior performance over previous methods, achieving higher recall rates and reduced latency.