Can Query Expansion Improve Generalization of Strong Cross-Encoder Rankers?

Query expansion has been widely used to improve the search results of first-stage retrievers, yet its influence on second-stage, crossencoder rankers remains under-explored.A recent work of Weller et al. 44 shows that current expansion techniques benefit weaker models such as DPR and BM25 but harm stronger rankers such as MonoT5.In this paper, we re-examine this conclusion and raise the following question: Can query expansion improve generalization of strong cross-encoder rankers?To answer this question, we first apply popular query expansion methods to state-of-the-art crossencoder rankers and verify the deteriorated zero-shot performance.We identify two vital steps for cross-encoders in the experiment: high-quality keyword generation and minimal-disruptive query modification.We show that it is possible to improve the generalization of a strong neural ranker, by prompt engineering and aggregating the ranking results of each expanded query via fusion.Specifically, we first call an instruction-following language model to generate keywords through a reasoning chain.Leveraging selfconsistency and reciprocal rank weighting, we further combine the ranking results of each expanded query dynamically.Experiments on BEIR and TREC Deep Learning 2019/2020 show that the nDCG@10 scores of both MonoT5 and RankT5 following these steps are improved, which points out a direction for applying query expansion to strong cross-encoder rankers.