ColBERT XM

ColBERT XM is a modular, multi-vector retrieval model designed to improve multilingual information retrieval, particularly for low-resource languages. Research focuses on enhancing its efficiency and effectiveness through techniques like improved training pipelines, aggressive compression methods, and token pruning to reduce storage needs while maintaining accuracy. This work addresses the limitations of existing models that struggle with multilingual data and resource constraints, ultimately aiming to make information retrieval more accessible and sustainable across diverse languages.