Data reflects the growing use of AI, including for search, too. A May 2024 Microsoft survey found that 75% of knowledge ...

Instructed Retriever leverages contextual memory for system-level specifications while using retrieval to access the broader ...

In this tutorial, we walk through the implementation of an Agentic Retrieval-Augmented Generation (RAG) system. We design it so that the agent does more than just retrieve documents; it actively ...

SPLADE-Index is an ultrafast index for SPLADE sparse retrieval models implemented in pure Python and powered by Scipy sparse matrices. It is built on top of the BM25s library. SPLADE is a neural ...

The applications of neural network models, shallow or deep, to information retrieval (IR) tasks falls under the purview of neural IR. Over the years, machine learning methods-including neural networks ...

Abstract: Private Information Retrieval (PIR) allows a client to retrieve an entry from a database held by a server without leaking which entry is being requested. Symmetric PIR (SPIR) is a stronger ...

What if the way we retrieve information from massive datasets could mirror the precision and adaptability of human reading—without relying on pre-built indexes or embeddings? OpenAI’s latest ...

Search is dead, long live search! Search isn’t what it used to be. Search engines no longer simply match keywords or phrases in user queries with webpages. We are moving well beyond the world of ...

Knowledge may be power, but information can also be overwhelming. Decision-makers often have access to so much potentially relevant data that they must choose what to ignore. Economists call this ...

For years, search engines and databases relied on essential keyword matching, often leading to fragmented and context-lacking results. The introduction of generative AI and the emergence of ...