  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.
