  Query expansion has been widely used to improve the search results of
first-stage retrievers, yet its influence on second-stage, cross-encoder
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 cross-encoder
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 self-consistency 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.
