3 citations · 7 across the 6 of their papers we have counts for
6 papers
Overview of the TREC 2025 Product Search and Recommendation Track
Dean E. Alvarez, Surya Kallumadi, Daniel Campos +4
In the past few years, consumers have moved the bulk of their product exploration and purchasing efforts online seeking speed, convenience, and price comparison with ease unimagina…
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…
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…
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…
Arctic-Embed: Scalable, Efficient, and Accurate Text Embedding Models
Luke Merrick, Danmei Xu, Gaurav Nuti +1
This report describes the training dataset creation and recipe behind the family of \texttt{arctic-embed} text embedding models (a set of five models ranging from 22 to 334 million…
Overview of the TREC 2023 Product Product Search Track
Daniel Campos, Surya Kallumadi, Corby Rosset +2
This is the first year of the TREC Product search track. The focus this year was the creation of a reusable collection and evaluation of the impact of the use of metadata and multi…