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Rerank-2.5 and rerank-2.5-lite: instruction-following rerankers

news.ycombinator.com Voyage Ai 2025-11-22 09:12:32

TL;DR – We are excited to introduce the rerank-2.5 series, which significantly improves upon rerank-2 ’s performance while also introducing instruction-following capabilities for the first time. On our standard suite of 93 retrieval datasets spanning multiple domains, rerank-2.5 and rerank-2.5-lite improve retrieval accuracy by 7.94% and 7.16% over Cohere Rerank v3.5. Furthermore, the new instruction-following feature allows users to steer the model’s output relevance scores using natural langua

Topics: following instruction lite rerank retrieval
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rerank-2.5 and rerank-2.5-lite: instruction-following rerankers

news.ycombinator.com Voyage Ai 2025-11-22 21:12:32

TL;DR – We are excited to introduce the rerank-2.5 series, which significantly improves upon rerank-2 ’s performance while also introducing instruction-following capabilities for the first time. On our standard suite of 93 retrieval datasets spanning multiple domains, rerank-2.5 and rerank-2.5-lite improve retrieval accuracy by 7.94% and 7.16% over Cohere Rerank v3.5. Furthermore, the new instruction-following feature allows users to steer the model’s output relevance scores using natural langua

Topics: following instruction lite rerank retrieval
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Beyond Retrieval: The Expanding Universe of Augmented Generation in AI

computer.org Laurel Tweed 2025-12-11 20:00:01

Introduction Standard large language models (LLMs) possess vast knowledge but struggle with limitations like hallucinations and accessing real-time information due to their static training data. This has spurred the development of dynamic AI architectures. Retrieval-Augmented Generation (RAG) has emerged as a key solution, integrating external knowledge into the generation process. However, the field is rapidly evolving beyond basic RAG. Newer models like RASG (Retrieval-Augmented Self-Generate

Topics: augmented generation knowledge rag retrieval
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Muvera: Making multi-vector retrieval as fast as single-vector search

news.ycombinator.com Unknown 2026-02-26 04:29:34

Neural embedding models have become a cornerstone of modern information retrieval (IR). Given a query from a user (e.g., “How tall is Mt Everest?”), the goal of IR is to find information relevant to the query from a very large collection of data (e.g., the billions of documents, images, or videos on the Web). Embedding models transform each datapoint into a single-vector “embedding”, such that semantically similar datapoints are transformed into mathematically similar vectors. The embeddings are

Topics: embedding multi retrieval similarity vector
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