@inproceedings{wang2023query,
author = {Wang, Liang and Yang, Nan and Wei, Furu},
title = {Query2doc: Query Expansion with Large Language Models},
booktitle = {EMNLP 2023},
year = {2023},
month = {March},
abstract = {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.},
url = {http://approjects.co.za/?big=en-us/research/publication/query2doc-query-expansion-with-large-language-models/},
}