  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 shown to be a
promising approach due to its ability to exploit knowledge inherent in large
language models. By taking inspiration from the success of ensemble prompting
strategies which have benefited many tasks, we investigate if they can help
improve query reformulation. In this context, we propose an ensemble based
prompting technique, GenQREnsemble which leverages paraphrases of a zero-shot
instruction to generate multiple sets of keywords ultimately improving
retrieval performance. We further introduce its post-retrieval variant,
GenQREnsembleRF to incorporate pseudo relevant feedback. On evaluations over
four IR benchmarks, we find that GenQREnsemble generates better reformulations
with relative nDCG@10 improvements up to 18% and MAP improvements upto 24% over
the previous zero-shot state-of-art. On the MSMarco Passage Ranking task,
GenQREnsembleRF shows relative gains of 5% MRR using pseudo-relevance feedback,
and 9% nDCG@10 using relevant feedback documents.
