Jina-ColBERT-v2: A General-Purpose Multilingual Late Interaction Retriever
Rohan Jha, Bo Wang, Michael Günther, Saba Sturua, Mohammad Kalim Akram, Han Xiao
2024-09-02
Summary
This paper talks about Jina-ColBERT-v2, a new multilingual model designed to improve how we retrieve information from documents in different languages.
What's the problem?
Information retrieval systems often struggle with efficiently finding relevant documents, especially when dealing with multiple languages. Traditional models can be slow and require a lot of resources, which makes them less effective for real-world applications.
What's the solution?
Jina-ColBERT-v2 enhances the existing ColBERT model by introducing improvements in its architecture and training methods. It uses a technique called late interaction scoring to efficiently compare queries and documents while reducing the amount of data that needs to be processed. This new model performs well on various retrieval tasks in both English and other languages, while also cutting down storage needs by up to 50%.
Why it matters?
This research is important because it provides a more efficient way to search for information across different languages, making it easier for users worldwide to access relevant content. By improving retrieval systems, Jina-ColBERT-v2 can help in many areas, including education, research, and information management.
Abstract
Multi-vector dense models, such as ColBERT, have proven highly effective in information retrieval. ColBERT's late interaction scoring approximates the joint query-document attention seen in cross-encoders while maintaining inference efficiency closer to traditional dense retrieval models, thanks to its bi-encoder architecture and recent optimizations in indexing and search. In this paper, we introduce several improvements to the ColBERT model architecture and training pipeline, leveraging techniques successful in the more established single-vector embedding model paradigm, particularly those suited for heterogeneous multilingual data. Our new model, Jina-ColBERT-v2, demonstrates strong performance across a range of English and multilingual retrieval tasks, while also cutting storage requirements by up to 50% compared to previous models.