Adaptive Re-Ranking with a Corpus Graph

TL;DR

This paper introduces GAR, a graph-based adaptive re-ranking method that enhances document retrieval by iteratively expanding candidate pools based on similarity, improving ranking performance with minimal additional costs.

Contribution

The paper proposes a novel adaptive re-ranking approach that overcomes recall limitations by dynamically expanding candidate pools using a graph-based similarity feedback mechanism.

Findings

Significantly improves re-ranking performance on MS MARCO dataset

Enhances nDCG by up to 8% with minimal computational overhead

Complementary to existing retrieval techniques

Abstract

Search systems often employ a re-ranking pipeline, wherein documents (or passages) from an initial pool of candidates are assigned new ranking scores. The process enables the use of highly-effective but expensive scoring functions that are not suitable for use directly in structures like inverted indices or approximate nearest neighbour indices. However, re-ranking pipelines are inherently limited by the recall of the initial candidate pool; documents that are not identified as candidates for re-ranking by the initial retrieval function cannot be identified. We propose a novel approach for overcoming the recall limitation based on the well-established clustering hypothesis. Throughout the re-ranking process, our approach adds documents to the pool that are most similar to the highest-scoring documents up to that point. This feedback process adapts the pool of candidates to those that may also yield high ranking scores, even if they were not present in the initial pool. It can also increase the score of documents that appear deeper in the pool that would have otherwise been skipped due to a limited re-ranking budget. We find that our Graph-based Adaptive Re-ranking (GAR) approach significantly improves the performance of re-ranking pipelines in terms of precision- and recall-oriented measures, is complementary to a variety of existing techniques (e.g., dense retrieval), is robust to its hyperparameters, and contributes minimally to computational and storage costs. For instance, on the MS MARCO passage ranking dataset, GAR can improve the nDCG of a BM25 candidate pool by up to 8% when applying a monoT5 ranker.