Query2doc: Query Expansion with Large Language Models
This addresses query disambiguation in information retrieval for users, but it is incremental as it builds on existing LLM and retrieval methods.
The paper tackles query expansion for retrieval systems by generating pseudo-documents using large language models, resulting in performance boosts of 3% to 15% for BM25 on datasets like MS-MARCO and TREC DL without fine-tuning.
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.