Query2doc: Query Expansion with Large Language Models
- URL: http://arxiv.org/abs/2303.07678v2
- Date: Wed, 11 Oct 2023 08:34:42 GMT
- Title: Query2doc: Query Expansion with Large Language Models
- Authors: Liang Wang, Nan Yang, Furu Wei
- Abstract summary: The proposed method first generates pseudo- documents by few-shot prompting large language models (LLMs)
query2doc boosts the performance of BM25 by 3% to 15% on ad-hoc IR datasets.
Our method also benefits state-of-the-art dense retrievers in terms of both in-domain and out-of-domain results.
- Score: 69.9707552694766
- License: http://creativecommons.org/licenses/by/4.0/
- 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.
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