Overview of Late Interaction Retrieval Models: ColBERT, ColPali, and ColQwen

AI Impact Summary

The document introduces ColBERT, ColPali, and ColQwen – late interaction retrieval models designed to balance the speed of no-interaction models with the contextual richness of full-interaction models. These models utilize a dual-encoder architecture with token-level embeddings and a ‘late interaction’ mechanism, where similarity scores are calculated between query and document tokens, offering improved accuracy and scalability compared to traditional dense retrieval methods. This approach is particularly relevant for RAG pipelines and applications requiring precise semantic retrieval.