Jina-ColBERT-v2: A General-Purpose Multilingual Late Interaction
  Retriever
Authors: name: Rohan Jha, name: Bo Wang, name: Michael Günther, name: Saba Sturua, name: Mohammad Kalim Akram, name: Han Xiao
Organization: Jina AI

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.