Arctic-Embed 2.0: Multilingual Retrieval Without Compromise

Autor:	Yu, Puxuan, Merrick, Luke, Nuti, Gaurav, Campos, Daniel
Rok vydání:	2024
Předmět:	Computer Science - Computation and Language Computer Science - Information Retrieval Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	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 works have suffered from degraded English retrieval quality, Arctic-Embed 2.0 delivers competitive retrieval quality on multilingual and English-only benchmarks, and supports Matryoshka Representation Learning (MRL) for efficient embedding storage with significantly lower compressed quality degradation compared to alternatives. We detail the design and implementation, presenting several important open research questions that arose during model development. We conduct experiments exploring these research questions and include extensive discussion aimed at fostering further discussion in this field. Comment: 10 pages, 5 figures, 3 tables
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2412.04506 Zobrazit plný text záznamu View this record from Arxiv