3 citations · 3 across the 3 of their papers we have counts for
3 papers
cs.IR2025
EnronQA: Towards Personalized RAG over Private Documents
Michael J. Ryan, Danmei Xu, Chris Nivera +1
Retrieval Augmented Generation (RAG) has become one of the most popular methods for bringing knowledge-intensive context to large language models (LLM) because of its ability to br…
cs.IR2025
ColBERT-serve: Efficient Multi-Stage Memory-Mapped Scoring
Kaili Huang, Thejas Venkatesh, Uma Dingankar +9
We study serving retrieval models, specifically late interaction models like ColBERT, to many concurrent users at once and under a small budget, in which the index may not fit in m…
cs.CL2024★ 3 cited
Arctic-Embed 2.0: Multilingual Retrieval Without Compromise
Puxuan Yu, Luke Merrick, Gaurav Nuti +1
This paper presents the training methodology of Arctic-Embed 2.0, a set of open-source text embedding models built for accurate and efficient multilingual retrieval. While prior wo…