The functionality of Zerank-1 is designed to perfectly fit the needs of a retrieval augmentation generation (RAG) system. By calculating the relevance scores of the "query-document" pairs, it is able to perform a secondary ranking of the initial search results, placing the most relevant documents at the top of the list. This feature filters out common distractions and low relevance content from the initial search, thus significantly improving the quality of the contextual information that is ultimately provided to the Large Language Model (LLM).
In typical application scenarios, Zerank-1 is deployed to perform fine-grained reordering of the first 100 retrieval results after the initial search, ensuring that the first 10 document fragments entered into the LLM are the most relevant content. This technical implementation greatly improves the accuracy and factuality of LLM-generated content, and solves the common problem of noise interference in RAG systems. The model is also particularly suitable for intelligent Q&A systems, document code de-duplication, and other business scenarios that require accurate semantic matching.
This answer comes from the articleZerank-1: A reordering model for improving the precision of search resultsThe
