Text retrieval is a long-standing research topic on information seeking, where a system is
required to return relevant information resources to user's queries in natural language. From …

There are now over 20 commercial vector database management systems (VDBMSs), all
produced within the past five years. But embedding-based retrieval has been studied for …

Current state-of-the-art document retrieval solutions mainly follow an index-retrieve
paradigm, where the index is hard to be directly optimized for the final retrieval target. In this …

The popularity of deep learning has led to the curation of a vast number of massive and
multifarious datasets. Despite having close-to-human performance on individual tasks …

This survey explores the synergistic potential of Large Language Models (LLMs) and Vector
Databases (VecDBs), a burgeoning but rapidly evolving research area. With the proliferation …

Since the advent of personal computing devices, intelligent personal assistants (IPAs) have
been one of the key technologies that researchers and engineers have focused on, aiming …

Embedding-based retrieval methods construct vector indices to search for document
representations that are most similar to the query representations. They are widely used in …

The development of Artificial Intelligence Generated Content (AIGC) has been facilitated by
advancements in model algorithms, scalable foundation model architectures, and the …

Graph-based approaches to nearest neighbor search are popular and powerful tools for
handling large datasets in practice, but they have limited theoretical guarantees. We study …

We propose CXL-ANNS, a software-hardware collaborative approach to enable highly
scalable approximate nearest neighbor search (ANNS) services. To this end, we first …