Query Reformulation (QR) is a set of techniques used to transform a user's
original search query to a text that better aligns with the user's intent and
improves their search experience. Recently, zero-shot QR has been a promising
approach due to its ability to exploit knowledge inherent in large language
models. Inspired by the success of ensemble prompting strategies which have
benefited other tasks, we investigate if they can improve query reformulation.
In this context, we propose two ensemble-based prompting techniques,
GenQREnsemble and GenQRFusion which leverage paraphrases of a zero-shot
instruction to generate multiple sets of keywords to improve retrieval
performance ultimately. We further introduce their post-retrieval variants to
incorporate relevance feedback from a variety of sources, including an oracle
simulating a human user and a "critic" LLM. We demonstrate that an ensemble of
query reformulations can improve retrieval effectiveness by up to 18% on
nDCG@10 in pre-retrieval settings and 9% on post-retrieval settings on multiple
benchmarks, outperforming all previously reported SOTA results. We perform
subsequent analyses to investigate the effects of feedback documents,
incorporate domain-specific instructions, filter reformulations, and generate
fluent reformulations that might be more beneficial to human searchers.
Together, the techniques and the results presented in this paper establish a
new state of the art in automated query reformulation for retrieval and suggest
promising directions for future research.

通过使用两种基于集成的提示技术（GenQREnsemble 和 GenQRFusion）来改进搜索查询的检索性能，利用反馈信息提供更好的搜索体验，并建立了自动查询改写的最新技术水平。