Jina-ColBERT-v2: A General-Purpose Multilingual Late Interaction Retriever
<|reference_start|>Jina-ColBERT-v2: A General-Purpose Multilingual Late Interaction Retriever: 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 work we propose a number of incremental improvements to the ColBERT model architecture and training pipeline, using methods shown to work in the more mature single-vector embedding model training paradigm, particularly those that apply to heterogeneous multilingual data or boost efficiency with little tradeoff. Our new model, Jina-ColBERT-v2, demonstrates strong performance across a range of English and multilingual retrieval tasks.<|reference_end|>
@article{jha2024jina-colbert-v2:,
    title={Jina-ColBERT-v2: A General-Purpose Multilingual Late Interaction
  Retriever},
    author={Rohan Jha, Bo Wang, Michael G"unther, Georgios Mastrapas, Saba
  Sturua, Isabelle Mohr, Andreas Koukounas, Mohammad Kalim Akram, Nan Wang and
  Han Xiao},
    journal={arXiv preprint arXiv:2408.16672},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.16672},
    primaryClass={cs.IR cs.AI cs.CL}
}