Lightweight pooling-aware fine-tuning with k-means on a single dataset enables up to 83% vector compression in ColBERT models with no retrieval accuracy loss and positive cross-dataset transfer.
MacQueen, Some methods for classification and analysis of multivariate observations, in: Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, Vol
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Learn to Pool: Lightweight Fine-Tuning for Flexible Multi-Vector Compression
Lightweight pooling-aware fine-tuning with k-means on a single dataset enables up to 83% vector compression in ColBERT models with no retrieval accuracy loss and positive cross-dataset transfer.