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Query expansion is a commonly-used technique in many search systems to betterrepresent users' information needs with additional query terms. Existingstudies for this task usually propose to expand a query with retrieved orgenerated contextual documents. However, both types of methods have clearlimitations. For retrieval-based methods, the documents retrieved with theoriginal query might not be accurate enough to reveal the search intent,especially when the query is brief or ambiguous. For generation-based methods,existing models can hardly be trained or aligned on a particular corpus, due tothe lack of corpus-specific labeled data. In this paper, we propose a novelLarge Language Model (LLM) based mutual verification framework for queryexpansion, which alleviates the aforementioned limitations. Specifically, wefirst design a query-query-document generation pipeline, which can effectivelyleverage the contextual knowledge encoded in LLMs to generate sub-queries andcorresponding documents from multiple perspectives. Next, we employ a mutualverification method for both generated and retrieved contextual documents,where 1) retrieved documents are filtered with the external contextualknowledge in generated documents, and 2) generated documents are filtered withthe corpus-specific knowledge in retrieved documents. Overall, the proposedmethod allows retrieved and generated documents to complement each other tofinalize a better query expansion. We conduct extensive experiments on threeinformation retrieval datasets, i.e., TREC-DL-2020, TREC-COVID, and MSMARCO.The results demonstrate that our method outperforms other baselinessignificantly.

MILL: Mutual Verification with Large Language Models for Zero-Shot Query Expansion