On Approximate Nearest Neighbour Selection for Multi-Stage Dense Retrieval

TL;DR

This paper explores using approximate nearest neighbour scores to efficiently rank candidate documents in dense retrieval, achieving similar effectiveness with half the candidate set and doubling retrieval speed.

Contribution

It introduces a method to reduce candidate set size in dense retrieval by leveraging ANN scores, improving efficiency without sacrificing effectiveness.

Findings

Reducing candidate set to 200 documents maintains effectiveness.

Using ANN scores speeds up retrieval by 2x.

Effective ranking is possible with smaller candidate sets.

Abstract

Dense retrieval, which describes the use of contextualised language models such as BERT to identify documents from a collection by leveraging approximate nearest neighbour (ANN) techniques, has been increasing in popularity. Two families of approaches have emerged, depending on whether documents and queries are represented by single or multiple embeddings. ColBERT, the exemplar of the latter, uses an ANN index and approximate scores to identify a set of candidate documents for each query embedding, which are then re-ranked using accurate document representations. In this manner, a large number of documents can be retrieved for each query, hindering the efficiency of the approach. In this work, we investigate the use of ANN scores for ranking the candidate documents, in order to decrease the number of candidate documents being fully scored. Experiments conducted on the MSMARCO passage ranking corpus demonstrate that, by cutting of the candidate set by using the approximate scores to only 200 documents, we can still obtain an effective ranking without statistically significant differences in effectiveness, and resulting in a 2x speedup in efficiency.