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

Late interaction retrieval models, such as ColBERT, ColPali, and ColQwen, combine the strengths of no-interaction and full-interaction models by offering both scalability and contextual richness in information retrieval processes. These models precompute token-level embeddings offline, allowing for efficient and precise retrieval by maintaining detailed semantic interactions at the token level, which enhances retrieval accuracy. ColBERT, a text-only model, builds on BERT by using a multi-vector approach to keep token-level embeddings, improving explainability and retrieval performance while requiring more storage. ColBERTv2 addresses storage issues through quantization and distillation from larger models, further optimizing retrieval efficiency. ColPali and ColQwen extend the late interaction concept to multimodal retrieval, treating PDF documents as images to handle complex documents with text and visuals, thereby simplifying the processing pipeline and improving contextual understanding for retrieval tasks. Despite higher storage requirements, these models are particularly beneficial for applications like legal document verification and multimodal RAG pipelines, where nuanced understanding and efficient retrieval are crucial.