rerank-2 and rerank-2-lite: the next generation of Voyage multilingual rerankers – Voyage AI
TL;DR — We’re excited to announce the Voyage 2 series of rerankers, rerank-2 and rerank-2-lite. When evaluated across 93 retrieval datasets spanning multiple domains, adding rerank-2 and rerank-2-lite on top of OpenAI’s latest embedding model (v3 large) improves the accuracy by an average of 13.89% and 11.86%, 2.3x and 1.7x the improvement attained by the latest Cohere reranker (English v3), respectively. Furthermore, rerank-2 and rerank-2-lite support context lengths of 16K and 8K tokens — 4x and 2x the context length of Cohere’s reranker.
Rerankers boost the quality of retrieval systems by refining the order of the initial search results. Earlier this year, we released our first-generation rerankers, rerank-lite-1 and rerank-1, both outperforming competing rerankers while offering at least 2x more context length and flexible token-based pricing.
Today, we are thrilled to introduce our Voyage 2 series of rerankers, rerank-2 and rerank-2-lite. rerank-2 is optimized for quality, improving accuracy atop OpenAI v3 large (text-embedding-3-large) by an average of 13.89% — 2.80%, 7.14%, and 15.61% more than rerank-1, Cohere v3 (rerank-english-v3.0), and BGE v2-m3 (bge-reranker-v2-m3) respectively. It supports a 16K-token combined context length for a query-document pair, with up to 4K tokens for the query.
