Papers with query expansion
NeuralQA: A Usable Library for Question Answering (Contextual Query Expansion + BERT) on Large Datasets (2020.emnlp-demos)
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| Challenge: | Existing tools for Question Answering (QA) have challenges that limit their use in practice. |
| Approach: | They propose a library that integrates with existing infrastructure and offers helpful defaults for QA subtasks. |
| Outcome: | NeuralQA integrates well with existing infrastructure and offers helpful defaults for QA subtasks. |
GOLFer: Smaller LMs-Generated Documents Hallucination Filter & Combiner for Query Expansion in Information Retrieval (2025.findings-acl)
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| Challenge: | Large language models (LLMs)-based query expansion for information retrieval necessitates larger, more advanced LLMs. |
| Approach: | They propose a method leveraging smaller open-source LMs for query expansion that augments queries with generated hypothetical documents with LLMs. |
| Outcome: | The proposed method outperforms existing methods on three web search and ten low-resource datasets and maintains competitive performance against larger LLMs. |
Exp4Fuse: A Rank Fusion Framework for Enhanced Sparse Retrieval using Large Language Model-based Query Expansion (2025.findings-acl)
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| Challenge: | Large Language Models (LLMs) have shown potential in generating hypothetical documents for query expansion, but their efficacy is dependent on the quality of the generated documents. |
| Approach: | They propose a fusion ranking framework that uses zero-shot LLM-based query expansion to enhance sparse retrieval. |
| Outcome: | The proposed framework surpasses existing LLM-based query expansion methods on three MS MARCO-related datasets and seven low-resource datasets. |
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. |
Vector Space Interpolation for Query Expansion (2022.aacl-short)
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| Challenge: | Topic-sensitive query set expansion is crucial for queries related to sensitive and emerging topics. |
| Approach: | They propose a method for topic-sensitive query set expansion using vector space interpolation. |
| Outcome: | The proposed method generates new queries about the sensitive topic by incorporating set diversity, which is not captured by traditional sentence-level augmentation methods such as paraphrasing or back-translation. |
Exploring the Best Practices of Query Expansion with Large Language Models (2024.findings-emnlp)
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| Challenge: | Large Language Models (LLMs) are foundational in language technologies, particularly in information retrieval (IR). |
| Approach: | They propose a framework that leverages large language models for query expansion . they use LLMs to generate multiple pseudo-references and integrate them with original queries . |
| Outcome: | The proposed framework enhances sparse and dense retrieval methods without pre-indexing. |
MMLF: Multi-query Multi-passage Late Fusion Retrieval (2025.findings-naacl)
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| Challenge: | Existing approaches to query expansion are limited in terms of lexical overlap. |
| Approach: | They propose a query expansion pipeline that generates sub-queries, expands them into pseudo-documents, retrieves them individually and aggregates results using reciprocal rank fusion. |
| Outcome: | The proposed pipeline improves on five BEIR benchmark datasets and achieves a maximum gain of up to 8%. |
Word2Passage: Word-level Importance Re-weighting for Query Expansion (2025.findings-acl)
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| Challenge: | Retrieval-augmented generation (RAG) enhances the quality of LLM generation by providing relevant chunks, but retrieving accurately from external knowledge remains challenging due to missing contextually important words in query expansion. |
| Approach: | They propose a method that generates word, sentence, and passage references for query expansion and assigns distinct importance scores to words based on their origin and characteristics. |
| Outcome: | The proposed method outperforms existing methods across datasets and LLM configurations, effectively enhancing retrieval accuracy and generation quality. |
Enhancing Retrieval-Augmented Generation: A Study of Best Practices (2025.coling-main)
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| Challenge: | Retrieval-augmented generation systems have shown remarkable advancements by integrating retrieval mechanisms into language models, enhancing their ability to produce more accurate and contextually relevant responses. |
| Approach: | They propose to integrate query expansion, various novel retrieval strategies, and a Contrastive In-Context Learning RAG to improve response quality. |
| Outcome: | The proposed RAGs incorporate query expansion, various novel retrieval strategies, and a novel Contrastive In-Context Learning RAG. |
Knowledge-Selective Pretraining for Attribute Value Extraction (2023.findings-emnlp)
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Hui Liu, Qingyu Yin, Zhengyang Wang, Chenwei Zhang, Haoming Jiang, Yifan Gao, Zheng Li, Xian Li, Chao Zhang, Bing Yin, William Wang, Xiaodan Zhu
| Challenge: | Existing methods for AVE are limited on rare attributes due to poor generalization ability. |
| Approach: | They propose to leverage pretraining and transfer learning to address weaknesses in existing methods. |
| Outcome: | The proposed method achieves new state-of-the-art performance without pretraining on rare attributes with limited training resources. |
UniRAG: Unified Query Understanding Method for Retrieval Augmented Generation (2025.acl-long)
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| Challenge: | Existing query augmentation methods face knowledge update lag and hallucinations in large language models (LLMs) Existing methods face two key challenges: (1) separation of query augmented and encoding tasks, which hinders information sharing and introduces cumulative errors; (2) difficulty of selecting optimal augmentation strategy for different scenarios. |
| Approach: | They propose a unified framework for query understanding in RAG that integrates internal and external knowledge to enhance query augmentation and encoding tasks. |
| Outcome: | The proposed framework outperforms traditional query augmentation methods in five knowledge-intensive benchmark tasks in both closed and open domain question answering. |
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. |
| Outcome: | Empirically, EAR improves top-5/20 accuracy by 3-8 and 5-10 points when compared to a vanilla query expansion model and a dense retrieval model. |
Multi-Level Information Retrieval Augmented Generation for Knowledge-based Visual Question Answering (2024.emnlp-main)
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| Challenge: | Knowledge-Aware Visual Question Answering about Entity tasks require two separate steps to generate accurate answers. |
| Approach: | They propose a multi-level information RAG approach that enhances answer generation through entity retrieval and query expansion. |
| Outcome: | The proposed approach improves answer generation through entity retrieval and query expansion. |
ThinkQE: Query Expansion via an Evolving Thinking Process (2025.findings-emnlp)
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| 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. |
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. |
| Outcome: | The proposed method improves retrieval accuracy and overall performance on four ODQA datasets and five QE methods. |
RADCoT: Retrieval-Augmented Distillation to Specialization Models for Generating Chain-of-Thoughts in Query Expansion (2024.lrec-main)
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| Challenge: | Large language models (LLMs) have demonstrated superior performance to that of small language models in information retrieval for various subtasks including dense retrieval, reranking, query expansion, and pseudo-document generation. |
| Approach: | They propose a retrieval-augmented model specialization that distills the capability of LLMs to generate the chain-of-thoughts (CoT) for query expansion into a RADCoT. |
| Outcome: | The proposed model can generate the chain-of-thoughts (CoT) for query expansion, reducing the burden of internalizing and retaining world knowledge in model parameters. |
QuDAR: Query-Wise Dual-Perspective Adaptive Retrieval (2026.acl-long)
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| Challenge: | Existing approaches to grounding large language models rely on static weights and a static retrieval component. |
| Approach: | They propose a dual-perspective adaptive retrieval framework that adapts along two perspectives: retriever type (sparse vs. dense) and query format (original v. expanded). |
| Outcome: | The proposed framework adapts along two perspectives: retriever type (sparse vs. dense) and query format (original v. expanded). |