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.

翻译：查询重构是一组用于将用户原始搜索查询转换为更符合用户意图并提升搜索体验文本的技术。近期，零样本查询重构因能利用大语言模型内在知识而展现出前景。受集成提示策略成功推动诸多任务的启发，我们探究其是否能助力改进查询重构。为此，我们提出基于集成的提示技术GenQREnsemble，通过利用零样本指令的释义生成多组关键词，最终提升检索性能。进一步，我们引入其后检索变体GenQREnsembleRF以融合伪相关反馈。在四个信息检索基准上的评估表明，GenQREnsemble相较于先前零样本最优方法，在nDCG@10上取得高达18%的相对提升，MAP提升达24%。在MSMarco段落排序任务中，GenQREnsembleRF通过伪相关反馈实现5%的MRR相对增益，通过相关反馈文档取得9%的nDCG@10相对提升。