Zerank-1 utilizes a Cross-Encoder architecture, a technology choice that gives it a significant advantage over traditional embedding models (dual encoders). While traditional embedding models generate vectors for queries and documents independently, and then determine relevance by calculating cosine similarity, Zerank-1's Cross-Encoder is able to process both the query and the document simultaneously, capturing the more complex interactions and semantic relationships between the two.
This architectural difference leads to a significant improvement in accuracy in reordering tasks. Although there is an increase in computational cost compared to dual-encoder implementations, the price is completely worth it for application scenarios that require high-precision sorting.Benchmarking results from ZeroEntropy show that Zerank-1 comprehensively outperforms traditional embedding modeling solutions across the board in all metrics of semantic search and relevance evaluation.
This answer comes from the articleZerank-1: A reordering model for improving the precision of search resultsThe
