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Query2doc: Query Expansion with Large Language Models

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arxiv 2303.07678 v2 pith:PL75D4CX submitted 2023-03-14 cs.IR cs.CL

Liang Wang , Nan Yang , Furu Wei This is my paper
classification cs.IRcs.CL
keywords queryllmspseudo-documentsquery2docdenseexpansionlanguagelarge
verification ladder T0 review T1 audit T2 compute T3 formal
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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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Cited by 17 Pith papers

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