5 papers
A Unified Benchmark for Privacy-preserving Vector Search
Anne-Marie Kermarrec, Rafael Pires, Mathis Randl +1
Vector search powers semantic search, recommendation systems, and retrieval-augmented generation (RAG). By design, the service answering a query sees both the query embedding and,…
Efficient Federated Search for Retrieval-Augmented Generation using Lightweight Routing
Akash Dhasade, Rachid Guerraoui, Anne-Marie Kermarrec +4
Large language models (LLMs) achieve remarkable performance across domains but remain prone to hallucinations and inconsistencies. Retrieval-augmented generation (RAG) mitigates th…
Catapults to the Rescue: Accelerating Vector Search by Exploiting Query Locality
Sami Abuzakuk, Anne-Marie Kermarrec, Rafael Pires +2
Graph-based indexing is the dominant approach for approximate nearest neighbor search in vector databases, offering high recall with low latency across billions of vectors. However…
Effective LoRA Adapter Routing using Task Representations
Akash Dhasade, Anne-Marie Kermarrec, Igor Pavlovic +4
Low-rank adaptation (LoRA) enables parameter efficient specialization of large language models (LLMs) through modular adapters, resulting in rapidly growing public adapter pools sp…
Leveraging Approximate Caching for Faster Retrieval-Augmented Generation
Shai Bergman, Anne-Marie Kermarrec, Diana Petrescu +4
Retrieval-augmented generation (RAG) improves the reliability of large language model (LLM) answers by integrating external knowledge. However, RAG increases the end-to-end inferen…