Reciprocal Rank Fusion explained: how hybrid search merges retriever results
Hybrid search systems use multiple retrievers — such as keyword-based BM25 and dense embedding models — each returning their own ranked list of documents for a query. Merging these lists by averaging…
Read the full articleYou might also wanna read
Advanced RAG — Understanding Reciprocal Rank Fusion in Hybrid Search
Today, let’s come back to one of my favorite generative AI topics: Retrieval Augmented Generation , or RAG for short. In RAG, the quality of
AI Search - AI Search now has hybrid search and relevance boosting
AI Search now supports hybrid search and relevance boosting, giving you more control over how results are found and ranked. Hybrid search Hy
Hybrid Search and Re-Ranking: Improving RAG Accuracy Beyond Semantic Search
When semantic search isn't enough for the RAG
towardsdatascience.com·1mo ago
KaLM-Reranker-V1: A Decoupled Encoder-Decoder Reranker for Efficient Document Retrieval
As retrieval systems scale, high-quality reranking becomes increasingly important. However, most existing rerankers, whether encoder-based o
arxiv.org·23d ago
Improving Ad-hoc Search Effectiveness for Conversational Information Retrieval via Model Merging
arXiv:2607.08540v1 Announce Type: cross Abstract: Conversational information retrieval is challenging since it requires the consideration of
Theoretical Analysis Shows Late-Interaction Retrieval Models Can Outperform Standard Vector Inner Products
Late-interaction retrieval models that use the MaxSim similarity function have shown strong empirical performance, often outperforming singl
arxiv.org·8d ago
Comments
Sign in to join the conversation.
No comments yet. Be the first.