| Challenge: | LLM-based methods often generate narrowly focused expansions that overlook these desiderata. |
| Approach: | They propose a test-time query expansion framework that promotes exploration and result diversity . ThinkQE encourages deeper and comprehensive semantic exploration and a corpus-interaction strategy that iteratively refines expansions . |
| Outcome: | The proposed framework outperforms prior approaches on diverse web search benchmarks. |
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Corpus-Steered Query Expansion with Large Language Models (2024.eacl-short)
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| Challenge: | Recent studies show query expansions generate hypothetical documents that answer queries as expansions. |
| Approach: | They propose a corpus-steered query expansion to promote incorporation of knowledge embedded within the corpus. |
| Outcome: | et al. analyzed corpus-based Query Expansion (CSQE) using LLMs to generate hypothetical documents that answer the query. |
BERT-QE: Contextualized Query Expansion for Document Re-ranking (2020.findings-emnlp)
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| Challenge: | Existing methods to expand query use pseudo relevance feedback (PRF) but they are under-equipped to evaluate the relevance of information pieces used for expansion. |
| Approach: | They propose a query expansion model that leverages the BERT model to select relevant document chunks for expansion. |
| Outcome: | The proposed model significantly outperforms existing models on the TREC Robust04 and GOV2 test collections. |
Event-Centric Query Expansion in Web Search (2023.acl-industry)
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| Challenge: | Existing studies rely on long-term search log mining to improve search experience . EQE system is a novel event retrieval framework that can select the best expansion from a significant amount of potential events quickly and accurately. |
| Approach: | They propose a QE system that uses a four-stage event retrieval framework . they collect news headlines and then refine a dual-tower semantic model to serve as an encoder . |
| Outcome: | The proposed system can select the best expansion from a significant amount of potential events quickly and accurately. |
ExpandR: Teaching Dense Retrievers Beyond Queries with LLM Guidance (2025.emnlp-main)
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| Challenge: | Existing methods for enhancing dense retrieval with query augmentation ignore the alignment between generation and ranking objectives. |
| Approach: | They propose a unified LLM-augmented dense retrieval framework that jointly optimizes both the LLM and the retriever. |
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When do Generative Query and Document Expansions Fail? A Comprehensive Study Across Methods, Retrievers, and Datasets (2024.findings-eacl)
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| Challenge: | Using large language models (LMs) for query or document expansion can improve generalization in information retrieval. |
| Approach: | They conduct the first comprehensive analysis of large language models (LMs) for query or document expansion. |
| Outcome: | The proposed expansions improve retrieval performance for weaker models but harm stronger models. |
Knowledge-Aware Query Expansion with Large Language Models for Textual and Relational Retrieval (2025.naacl-long)
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| Challenge: | Existing methods to generate query expansions focus on enhancing textual similarities between search queries and document corpus, overlooking document relations. |
| Approach: | They propose a knowledge-aware query expansion framework augmenting LLMs with structured document relations from knowledge graph (KG) they leverage document texts as rich KG node representations and use document-based relation filtering for their method. |
| Outcome: | The proposed framework augments LLMs with structured document relations from knowledge graph (KG) Extensive experiments on three datasets of diverse domains show the advantages compared against state-of-the-art methods on textual and relational semi-structured retrieval. |
Not All Terms Matter: Recall-Oriented Adaptive Learning for PLM-aided Query Expansion in Open-Domain Question Answering (2025.acl-long)
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| Challenge: | Open-domain question answering (ODQA) systems typically adopt a retriever-reader architecture, where the retriever finds relevant documents, and the reader extracts or synthesizes answers. |
| Approach: | They propose a method that iteratively adjusts the importance weights of QE terms based on their relevance, refining term distinction and enhancing the separation of relevant terms. |
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Expand, Rerank, and Retrieve: Query Reranking for Open-Domain Question Answering (2023.findings-acl)
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| Challenge: | Empirically, EAR improves top-5/20 accuracy by 3-8 and 5-10 points . dense retrievers are limited by their inability to perform semantic matching for relevant passages that have low lexical overlap with the query. |
| Approach: | They propose a query expansion and reranking approach for improving passage retrieval with the application to open-domain question answering. |
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Analyze, Generate and Refine: Query Expansion with LLMs for Zero-Shot Open-Domain QA (2024.findings-acl)
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| Challenge: | Existing methods like GAR and EAR rely heavily on supervised training and struggle to maintain effectiveness across domains and datasets. |
| Approach: | They propose a QE approach based on a three-step prompting strategy to enhance query expansion by broadening the scope of queries with additional relevant texts. |
| Outcome: | The proposed approach outperforms state-of-the-art methods in out-domain zero-shot scenarios and outperformed existing methods in end-to-end evaluations. |
Query2doc: Query Expansion with Large Language Models (2023.emnlp-main)
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| Challenge: | Existing methods for sparse and dense retrieval have limited success on popular datasets. |
| Approach: | They propose a query expansion approach that generates pseudo-documents by few-shot prompting large language models and then expands the query with generated pseudo-docs. |
| Outcome: | The proposed method boosts the performance of BM25 on ad-hoc IR datasets by 3% to 15% without any model fine-tuning. |