Summary of Arctic-embed 2.0: Multilingual Retrieval Without Compromise, by Puxuan Yu and Luke Merrick and Gaurav Nuti and Daniel Campos
Arctic-Embed 2.0: Multilingual Retrieval Without Compromise
by Puxuan Yu, Luke Merrick, Gaurav Nuti, Daniel Campos
First submitted to arxiv on: 3 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Information Retrieval (cs.IR); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents a set of open-source text embedding models called Arctic-Embed 2.0, designed for accurate and efficient multilingual retrieval. Unlike previous works that suffered from degraded English retrieval quality, Arctic-Embed 2.0 achieves competitive retrieval quality on multilingual and English-only benchmarks while supporting Matryoshka Representation Learning (MRL) for efficient embedding storage with lower compressed quality degradation compared to alternatives. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Arctic-Embed 2.0 is a new tool that helps computers understand and find information in many languages quickly and accurately. It’s better than previous tools because it still works well even when searching only in English. This paper tells us how the researchers built this tool and what they learned along the way. |
Keywords
» Artificial intelligence » Embedding » Representation learning
