In the two-stage architecture of modern intelligent search systems, Zerank-1 assumes a crucial post-processing role. The first stage of the retrieval system (typically using the BM25 algorithm or vector similarity search) seeks speed and recall, generating hundreds or thousands of potentially relevant documents. zerank-1 then follows up with a fine-grained evaluation of each of these candidate documents, calculating a precise relevance score for each document with respect to the query, and then reordering based on the score.
This two-stage process with clear division of labor fully combines the advantages of speed and precision, realizing the search experience of "wide before fine". zerank-1, as the core component of the second stage, has a relatively large amount of computation and a slightly slower processing speed, but the accuracy improvement brought by it makes it become the quality assurance link of the whole search process, which effectively solves the problem of contamination caused by the general search system of "killing one thousand results rather than one mistake". This effectively solves the problem of contamination of results caused by the general search system, which "prefers to make a thousand mistakes".
This answer comes from the articleZerank-1: A reordering model for improving the precision of search resultsThe
