Query Expansion

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1 paper in the last four weeks, against 2 the four weeks before. 0.0% of all new papers.

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Towards Explaining Query Expansion Performance in Information Retrieval
Oct 7, 2026Sourav Saha, Aditya Dutta, Soumajit Pramanik +1Information Retrieval Query Expansion
Indian Statistical Institute, Kolkata, India · IIT Bombay, Bombay, India · IIT Bhilai, Chhattisgarh, India
Generated Query Expansion Still Helps Strong Sparse Retrieval: A Controlled Study with SPLADE-v3
Sep 29, 2026Ryan C. Barron, Cade W. Trotter, Maksim E. Eren +3Learned Sparse Retrieval Lexical Retrieval
Computational Intelligence & Modeling, Los Alamos National Laboratory, Los Alamos, New Mexico, USA. · Modeling and Observations of Earth Systems, Los Alamos National Laboratory, Los Alamos, New Mexico, USA. · Fluid Dynamics and Solid Mechanics, Los Alamos National Laboratory, Los Alamos, New Mexico, USA. +2
Staged Linguistic Seeding: Grounded Query Expansion for Verified-Unit QA in AI Contact Centers
Sep 1, 2026Hyeonseop Yoon, Jeong-Eun ParkCustomer Support Automation Question Answering
Independent Researcher Seoul, South Korea
Query Expansion Should Be Coordinated: Dense Expands, Sparse Anchors
Aug 16, 2026Chunran ZhangRetrieval-Augmented Generation RAG Evaluation
School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu, China
Retrieval Augmented Biomedical Question Answering with Weak Question Recovery and Neural Reranking for BioASQ Task 14b
Aug 2, 2026Xueying Zhao, Lee Mai, Balaji AnandganeshRetrieval-Augmented Generation Learning to Rank
Georgia Institute of Technology, North Ave NW, Atlanta, GA 30332
CeQe: Grounding Lexical Retrieval in Semantic Evidence
Aug 1, 2026Adam Kahirov, Umesh Deshpande, Swaminathan SundararamanLexical Retrieval Information Retrieval
IBM Research
Improving Item Discoverability in e-Commerce Search via Related Intent Generation
Jul 29, 2026Ji Xin, Xiao Xiao, Ishan Bhatt +6E-Commerce Search Information Retrieval
Instacart Toronto, Canada · Instacart San Diego, USA · Instacart San Francisco, USA +1
Beyond the Reranker: Do RAG Retrieval Enhancements Help Once a Strong Reranker Is Present?
Jun 14, 2026Sadanand Singh, Allam Reddy, Manan ChopraCross-Encoder Reranking Learning to Rank
Cascade Research
ADORE: Iterative Query Expansion with Retrieval-Grounded Relevance Feedback
Jun 11, 2026Amin Bigdeli, Negar Arabzadeh, Radin Hamidi Rad +3Information Retrieval Query Expansion
University of Waterloo · University of California, Berkeley · Mila – Quebec AI Institute +1
STORM: Stepwise Token Optimization with Reward-Guided Beam Search
Jun 9, 2026Arthur Satouf, Giulio D'Erasmo, Yuxuan Zong +3Language Model Decoding Lexical Retrieval
MILA – Quebec AI Institute & ILLS, Canada · 5Sapienza, University of Rome, Italy · 4Sorbonne Université & ISIR & CNRS, France +2
MARQUIS: A Three-Stage Pipeline for Video Retrieval-Augmented Generation
May 17, 2026Debashish Chakraborty, Dengjia Zhang, Jialiang Jin +7Multimodal RAG Retrieval-Augmented Generation
Johns Hopkins University · Human Language Technology Center of Excellence
Superintelligent Retrieval Agent: The Next Frontier of Agentic Retrieval
May 7, 2026Zeyu Yang, Qi Ma, Jason Chen +1Agentic Retrieval Information Retrieval
Meta Superintelligence Labs · Rice University · Work done at Meta
Open-SAT: LLM-Guided Query Embedding Refinement for Open-Vocabulary Object Retrieval in Satellite Imagery
May 6, 2026Md Adnan Arefeen, Biplob Debnath, Ravi K. Rajendran +2Remote Sensing Image Understanding Image-Text Retrieval
NEC Laboratories America Princeton, NJ, USA
MTRACE: Multilingual Retrieval-Augmented Generation for Temporally Diverse Text Corpora
Dec 14, 2025Souhail Bakkali, Anthony MudetRetrieval-Augmented Generation Multilingual IR
Univ Rennes, CNRS, IRISA - UMR 6074, Rennes, France · L3i-lab, La Rochelle Universit´e, France
Rethinking On-policy Optimization for Query Augmentation
Oct 20, 2025Zhichao Xu, Shengyao Zhuang, Xueguang Ma +6RL for Language Models LLM Prompting
University of Utah · 2The University of Queensland · University of Waterloo +5

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Towards Explaining Query Expansion Performance in Information Retrieval

Sourav Saha, Aditya Dutta, Soumajit Pramanik, Mandar Mitra

Indian Statistical Institute, Kolkata, India · IIT Bombay, Bombay, India · IIT Bhilai, Chhattisgarh, India

Information Retrieval Query Expansion IR Evaluation

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Query Expansion (QE) techniques have long been widely used in Information Retrieval (IR) to address the vocabulary mismatch problem. They remain relevant in modern retrieval systems, including those based on large language models (LLMs). However, no single QE method consistently outperforms others across all queries. This work seeks to explain the variation in QE performance through two complementary perspectives. The first is the concept of an Ideal Expanded Query (IEQ)--a hypothetical query that maximizes retrieval effectiveness with a downstream BM25 retrieval model. The second is a separability perspective, which quantifies how distinctly relevant and non-relevant documents are scored for a given expanded query using Cohen's (d). We develop a separability measure and practical formulations to approximate the IEQ and investigate how these factors relate to retrieval effectiveness. Extensive experiments on the TREC Robust collection, TREC DL 2019-2022 passage collections, and TREC DL 2019-2020 document collections reveal several interesting patterns. In particular, we find that expanded queries that are closer to the ideal expanded query tend to achieve higher retrieval effectiveness. We further show that the separability of relevant and non-relevant documents provides a complementary perspective for understanding QE performance.

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