ColBERTv2: Effective and Efficient Retrieval via Lightweight Late Interaction (2022.naacl-main)
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| Challenge: | Neural information retrieval (IR) methods encode queries and documents into single vectors, but late interaction models produce multi-vector representations at the granularity of each token. |
| Approach: | They propose a retrieval method that couples an aggressive residual compression mechanism with a denoised supervision strategy to improve the quality and space footprint of late interaction. |
| Outcome: | The proposed retriever improves quality and space footprint of late interaction models while reducing space footprint by 6–10x. |
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Ahmed Masry, Megh Thakkar, Patrice Bechard, Sathwik Tejaswi Madhusudhan, Rabiul Awal, Shambhavi Mishra, Akshay Kalkunte Suresh, Srivatsava Daruru, Enamul Hoque, Spandana Gella, Torsten Scholak, Sai Rajeswar
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