Efficient Nearest Neighbor Search for Cross-Encoder Models using Matrix Factorization

• URL: http://arxiv.org/abs/2210.12579v1
• Date: Sun, 23 Oct 2022 00:32:04 GMT
• Title: Efficient Nearest Neighbor Search for Cross-Encoder Models using Matrix Factorization
• Authors: Nishant Yadav, Nicholas Monath, Rico Angell, Manzil Zaheer and Andrew McCallum
• Abstract summary: We present an approach that avoids the use of a dual-encoder for retrieval, relying solely on the cross-encoder. Our approach provides test-time recall-vs-computational cost trade-offs superior to the current widely-used methods.
• Score: 60.91600465922932
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Efficient k-nearest neighbor search is a fundamental task, foundational for many problems in NLP. When the similarity is measured by dot-product between dual-encoder vectors or $\ell_2$-distance, there already exist many scalable and efficient search methods. But not so when similarity is measured by more accurate and expensive black-box neural similarity models, such as cross-encoders, which jointly encode the query and candidate neighbor. The cross-encoders' high computational cost typically limits their use to reranking candidates retrieved by a cheaper model, such as dual encoder or TF-IDF. However, the accuracy of such a two-stage approach is upper-bounded by the recall of the initial candidate set, and potentially requires additional training to align the auxiliary retrieval model with the cross-encoder model. In this paper, we present an approach that avoids the use of a dual-encoder for retrieval, relying solely on the cross-encoder. Retrieval is made efficient with CUR decomposition, a matrix decomposition approach that approximates all pairwise cross-encoder distances from a small subset of rows and columns of the distance matrix. Indexing items using our approach is computationally cheaper than training an auxiliary dual-encoder model through distillation. Empirically, for k > 10, our approach provides test-time recall-vs-computational cost trade-offs superior to the current widely-used methods that re-rank items retrieved using a dual-encoder or TF-IDF.

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