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#235

Neural information retrieval (IR) has greatly advanced search and other knowledge-intensive language tasks. While many neural IR methods encode queries and documents into single-vector representations, late interaction models produce multi-vector representations at the granularity of each token and decompose relevance modeling into scalable token-level computations. This decomposition has been shown to make late interaction more effective, but it inflates the space footprint of these models by an order of magnitude. ColBERTv2 has a retriever that couples an aggressive residual compression mechanism with a denoised supervision strategy to simultaneously improve the quality and space footprint of late interaction. ColBERTv2 is evaluated across a wide range of benchmarks, establishing state-of-the-art quality within and outside the training domain while reducing the space footprint of late interaction models by 6–10×. In this video, I talk about the following: How does multi-vector retrieval work with maxSim? Modeling and Indexing in ColBERTv2. Representation computation and Retrieval in ColBERTv2. MS MARCO, BEIR and LoTTE datasets. How does ColBERTv2 perform? For more details, please look at https://arxiv.org/pdf/2112.01488 Santhanam, Keshav, Omar Khattab, Jon Saad-Falcon, Christopher Potts, and Matei Zaharia. "ColBERTv2: Effective and Efficient Retrieval via Lightweight Late Interaction." In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 3715-3734. 2022.