This paper introduces a simple yet effective query expansion approach, denoted as query2doc, to improve both sparse and dense retrieval systems. The proposed method first generates pseudo-documents by few-shot prompting large language models (LLMs), and then expands the query with generated pseudo-documents. LLMs are trained on web-scale text corpora and are adept at knowledge memorization. The pseudo-documents from LLMs often contain highly relevant information that can aid in query disambiguation and guide the retrievers. Experimental results demonstrate that query2doc boosts the performance of BM25 by 3% to 15% on ad-hoc IR datasets, such as MS-MARCO and TREC DL, without any model fine-tuning. Furthermore, our method also benefits state-of-the-art dense retrievers in terms of both in-domain and out-of-domain results.

翻译：本文介绍了一种简单而有效的查询扩展方法,称为查询2doc,目的是改进稀少和密集的检索系统。提议的方法首先通过几发发促动大型语言模型(LLMS)生成假文件,然后用生成的伪文件扩大查询范围。LMS在网络规模的文本公司中接受培训,并善于知识记忆化。LMS的伪文件通常包含高度相关的信息,有助于查询脱节并引导检索者。实验结果显示,查询2doc在诸如MS-MARCO和TREC DL等临时IR数据集中将BM25的性能提高3%至15%,而没有任何模型微调。此外,我们的方法还有利于最新的高密度检索器,无论是在内部还是外部结果方面都是如此。</s>