activity
20242026
most citedLeveraging Approximate Caching for Faster Retrieval-Augmented Generation

4 citations · 4 across the 5 of their papers we have counts for

collaborators

6 papers

cs.CR2026

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,…

cs.DB2026

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…

cs.LG2026

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…

cs.DB2025★ 4 cited

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…

cs.LG2025

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…

cs.SI2024

#TeamFollowBack: Detection & Analysis of Follow Back Accounts on Social Media

Tuğrulcan Elmas, Mathis Randl, Youssef Attia

Follow back accounts inflate their follower counts by engaging in reciprocal followings. Such accounts manipulate the public and the algorithms by appearing more popular than they…