title: Jina-ColBERT-v2: A General-Purpose Multilingual Late Interaction
    Retriever
summary: 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.
paper_authors._id: 66d585b7092cc13d0f961d44
paper_authors.name: Rohan Jha
paper_authors._id: 66d585b7092cc13d0f961d45
paper_authors.name: Bo Wang
paper_authors.status: extracted_confirmed
paper_authors.statusLastChangedAt: 2024-10-02T07:11:25.862Z
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paper_authors.fullname: Bo Wang
paper_authors.type: user
paper_authors.user: bwang0911
paper_authors._id: 66d585b7092cc13d0f961d46
paper_authors.name: Michael Günther
paper_authors.status: admin_assigned
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paper_authors.fullname: Michael Günther
paper_authors.type: user
paper_authors.user: michael-guenther
paper_authors._id: 66d585b7092cc13d0f961d47
paper_authors.name: Saba Sturua
paper_authors.status: admin_assigned
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paper_authors.fullname: Saba Sturua
paper_authors.type: user
paper_authors.user: jupyterjazz
paper_authors._id: 66d585b7092cc13d0f961d48
paper_authors.name: Mohammad Kalim Akram
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-09-02T09:35:23.144Z
paper_authors._id: 64d22f33032a420d1863b6ea
paper_authors.avatarUrl: /avatars/ed3eaf4bab70dd6ab9a2b67b5928e4fb.svg
paper_authors.fullname: Mohammad Kalim Akram
paper_authors.type: user
paper_authors.user: makram93
paper_authors._id: 66d585b7092cc13d0f961d49
paper_authors.name: Han Xiao
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-09-02T09:35:38.480Z
paper_authors._id: 603763514de52ff951d89793
paper_authors.avatarUrl: /avatars/4484b361bb887fc73a6d38d953bfe9e0.svg
paper_authors.fullname: Han Xiao
paper_authors.type: user
paper_authors.user: hanxiao
paper_title: Jina-ColBERT-v2: A General-Purpose Multilingual Late Interaction
    Retriever
paper_summary: 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.
paper_ai_summary: Jina-ColBERT-v2 enhances ColBERT's performance and reduces storage requirements through improvements to its architecture and training pipeline, benefiting multilingual retrieval tasks.
paper_ai_keywords: multi-vector dense models ColBERT late interaction scoring joint query-document attention cross-encoders bi-encoder architecture indexing search single-vector embedding Jina-ColBERT-v2 heterogeneous multilingual data