Unlocking the State-of-the-Art Reranker: Introducing the Vectara Multilingual Reranker_v1

The Vectara Multilingual Reranker_v1 is a newly launched reranking model designed to enhance the precision of retrieval-augmented generation (RAG) pipelines by refining high-recall results, particularly excelling in multilingual datasets with a ~30% improvement in Normalized Discounted Cumulative Gain (NDCG) and a ~10% uplift for English datasets. Extensive benchmarking against renowned rerankers like Cohere Rerank 3 and Mono MT5 demonstrates its superior performance, ranking top for almost all English datasets and in the top two for multilingual datasets. Despite its precision, the reranker introduces a latency of approximately 100ms when reranking 25 results, a trade-off users can manage by adjusting the number of results reranked. Available exclusively to Scale-trial or Scale customers, the reranker can be accessed via the Vectara console or API, with comprehensive setup instructions provided to optimize its integration.