A comprehensive guide covering multi-vector retrieval systems introduced in 2021. Learn how token-level contextualized embeddings enabled fine-grained matching, the ColBERT late interaction mechanism that combined semantic and lexical matching, how multi-vector retrievers addressed limitations of single-vector dense retrieval, and their lasting impact on modern retrieval architectures.
This article is part of the free-to-read History of Language AI book
Reference
About the author: Michael Brenndoerfer
All opinions expressed here are my own and do not reflect the views of my employer.
Michael currently works as an Associate Director of Data Science at EQT Partners in Singapore, where he drives AI and data initiatives across private capital investments.
With over a decade of experience spanning private equity, management consulting, and software engineering, he specializes in building and scaling analytics capabilities from the ground up. He has published research in leading AI conferences and holds expertise in machine learning, natural language processing, and value creation through data.
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