DESSERT: An Efficient Algorithm for Vector Set Search with Vector Set Queries

We study the problem of textitvector set search with textitvector set queries. This task is analogous to traditional near-neighbor search, with the exception that both the query and each element in the collection are textitsets of vectors. We identify this problem as a core subroutine for semantic search applications and find that existing solutions are unacceptably slow. Towards this end, we present a new approximate search algorithm, DESSERT (bf DESSERT bf Effeciently bf Searches bf Sets of bf Embeddings via bf Retrieval bf Tables). DESSERT is a general tool with strong theoretical guarantees and excellent empirical performance. When we integrate DESSERT into ColBERT, a state-of-the-art semantic search model, we find a 2-5x speedup on the MS MARCO and LoTTE retrieval benchmarks with minimal loss in recall, underscoring the effectiveness and practical applicability of our proposal.