Counterfactual Online Learning to Rank. - Shengyao Zhuang; Guido Zuccon (ECIR (1), 2020)

Federated Online Learning to Rank with Evolution Strategies: A Reproducibility Study. - Shuyi Wang 001; Shengyao Zhuang; Guido Zuccon (ECIR (2), 2021)

Deep Query Likelihood Model for Information Retrieval. - Shengyao Zhuang; Hang Li 009; Guido Zuccon (ECIR (2), 2021)

Dealing with Typos for BERT-based Passage Retrieval and Ranking. - Shengyao Zhuang; Guido Zuccon (EMNLP (1), 2021)

Effective and Privacy-preserving Federated Online Learning to Rank. - Shuyi Wang 001; Bing Liu 025; Shengyao Zhuang; Guido Zuccon (ICTIR, 2021)

BERT-based Dense Retrievers Require Interpolation with BM25 for Effective Passage Retrieval. - Shuai Wang 032; Shengyao Zhuang; Guido Zuccon (ICTIR, 2021)

How do Online Learning to Rank Methods Adapt to Changes of Intent? - Shengyao Zhuang; Guido Zuccon (SIGIR, 2021)

TILDE: Term Independent Likelihood moDEl for Passage Re-ranking. - Shengyao Zhuang; Guido Zuccon (SIGIR, 2021)

Fast Passage Re-ranking with Contextualized Exact Term Matching and Efficient Passage Expansion. - Shengyao Zhuang; Guido Zuccon (CoRR, 2021)

Pseudo-Relevance Feedback with Dense Retrievers in Pyserini. - Hang Li 009; Shengyao Zhuang; Xueguang Ma; Jimmy Lin; Guido Zuccon (ADCS, 2022)

Robustness of Neural Rankers to Typos: A Comparative Study. - Shengyao Zhuang; Xinyu Mao; Guido Zuccon (ADCS, 2022)

Improving Query Representations for Dense Retrieval with Pseudo Relevance Feedback: A Reproducibility Study. - Hang Li 009; Shengyao Zhuang; Ahmed Mourad; Xueguang Ma; Jimmy Lin; Guido Zuccon (ECIR (1), 2022)

To Interpolate or not to Interpolate: PRF, Dense and Sparse Retrievers. - Hang Li 009; Shuai Wang 032; Shengyao Zhuang; Ahmed Mourad; Xueguang Ma; Jimmy Lin; Guido Zuccon (SIGIR, 2022)

Reduce, Reuse, Recycle: Green Information Retrieval Research. - Harrisen Scells; Shengyao Zhuang; Guido Zuccon (SIGIR, 2022)

Implicit Feedback for Dense Passage Retrieval: A Counterfactual Approach. - Shengyao Zhuang; Hang Li 009; Guido Zuccon (SIGIR, 2022)

CharacterBERT and Self-Teaching for Improving the Robustness of Dense Retrievers on Queries with Typos. - Shengyao Zhuang; Guido Zuccon (SIGIR, 2022)

Asyncval: A Toolkit for Asynchronously Validating Dense Retriever Checkpoints During Training. - Shengyao Zhuang; Guido Zuccon (SIGIR, 2022)

Bridging the Gap Between Indexing and Retrieval for Differentiable Search Index with Query Generation. - Shengyao Zhuang; Houxing Ren; Linjun Shou; Jian Pei 001; Ming Gong 001; Guido Zuccon; Daxin Jiang (CoRR, 2022)

Reinforcement online learning to rank with unbiased reward shaping. - Shengyao Zhuang; Zhihao Qiao; Guido Zuccon (Inf. Retr. J., 2022)

Open-source Large Language Models are Strong Zero-shot Query Likelihood Models for Document Ranking. - Shengyao Zhuang; Bing Liu 025; Bevan Koopman; Guido Zuccon (EMNLP (Findings), 2023)

Exploring the Representation Power of SPLADE Models. - Joel Mackenzie; Shengyao Zhuang; Guido Zuccon (ICTIR, 2023)

Beyond CO2 Emissions: The Overlooked Impact of Water Consumption of Information Retrieval Models. - Guido Zuccon; Harrisen Scells; Shengyao Zhuang (ICTIR, 2023)

Selecting which Dense Retriever to use for Zero-Shot Search. - Ekaterina Khramtsova; Shengyao Zhuang; Mahsa Baktashmotlagh; Xi Wang 021; Guido Zuccon (SIGIR-AP, 2023)

Typos-aware Bottlenecked Pre-Training for Robust Dense Retrieval. - Shengyao Zhuang; Linjun Shou; Jian Pei 001; Ming Gong 001; Houxing Ren; Guido Zuccon; Daxin Jiang (SIGIR-AP, 2023)

Augmenting Passage Representations with Query Generation for Enhanced Cross-Lingual Dense Retrieval. - Shengyao Zhuang; Linjun Shou; Guido Zuccon (SIGIR, 2023)

Team IELAB at TREC Clinical Trial Track 2023: Enhancing Clinical Trial Retrieval with Neural Rankers and Large Language Models. - Shengyao Zhuang; Bevan Koopman; Guido Zuccon (TREC, 2023)

Pseudo Relevance Feedback with Deep Language Models and Dense Retrievers: Successes and Pitfalls. - Hang Li 009; Ahmed Mourad; Shengyao Zhuang; Bevan Koopman; Guido Zuccon (ACM Trans. Inf. Syst., 2023)

Zero-Shot Generative Large Language Models for Systematic Review Screening Automation. - Shuai Wang 032; Harrisen Scells; Shengyao Zhuang; Martin Potthast; Bevan Koopman; Guido Zuccon (ECIR (1), 2024)

PromptReps: Prompting Large Language Models to Generate Dense and Sparse Representations for Zero-Shot Document Retrieval. - Shengyao Zhuang; Xueguang Ma; Bevan Koopman; Jimmy Lin; Guido Zuccon (EMNLP, 2024)

Understanding and Mitigating the Threat of Vec2Text to Dense Retrieval Systems. - Shengyao Zhuang; Bevan Koopman; Xiaoran Chu; Guido Zuccon (SIGIR-AP, 2024)

Embark on DenseQuest: A System for Selecting the Best Dense Retriever for a Custom Collection. - Ekaterina Khramtsova; Teerapong Leelanupab; Shengyao Zhuang; Mahsa Baktashmotlagh; Guido Zuccon (SIGIR, 2024)

Leveraging LLMs for Unsupervised Dense Retriever Ranking. - Ekaterina Khramtsova; Shengyao Zhuang; Mahsa Baktashmotlagh; Guido Zuccon (SIGIR, 2024)

Revisiting Document Expansion and Filtering for Effective First-Stage Retrieval. - Watheq Mansour; Shengyao Zhuang; Guido Zuccon; Joel Mackenzie (SIGIR, 2024)

Dense Retrieval with Continuous Explicit Feedback for Systematic Review Screening Prioritisation. - Xinyu Mao; Shengyao Zhuang; Bevan Koopman; Guido Zuccon (SIGIR, 2024)

FeB4RAG: Evaluating Federated Search in the Context of Retrieval Augmented Generation. - Shuai Wang 032; Ekaterina Khramtsova; Shengyao Zhuang; Guido Zuccon (SIGIR, 2024)

Large Language Models Based Stemming for Information Retrieval: Promises, Pitfalls and Failures. - Shuai Wang 032; Shengyao Zhuang; Guido Zuccon (SIGIR, 2024)

A Setwise Approach for Effective and Highly Efficient Zero-shot Ranking with Large Language Models. - Shengyao Zhuang; Honglei Zhuang; Bevan Koopman; Guido Zuccon (SIGIR, 2024)

Large Language Models for Stemming: Promises, Pitfalls and Failures. - Shuai Wang 032; Shengyao Zhuang; Guido Zuccon (CoRR, 2024)

A Systematic Investigation of Distilling Large Language Models into Cross-Encoders for Passage Re-ranking. - Ferdinand Schlatt; Maik Fröbe; Harrisen Scells; Shengyao Zhuang; Bevan Koopman; Guido Zuccon; Benno Stein 001; Martin Potthast; Matthias Hagen (CoRR, 2024)

Does Vec2Text Pose a New Corpus Poisoning Threat? - Shengyao Zhuang; Bevan Koopman; Guido Zuccon (CoRR, 2024)

Starbucks: Improved Training for 2D Matryoshka Embeddings. - Shengyao Zhuang; Shuai Wang 032; Bevan Koopman; Guido Zuccon (CoRR, 2024)

AgAsk: an agent to help answer farmer's questions from scientific documents. - Bevan Koopman; Ahmed Mourad; Hang Li 009; Anton van der Vegt; Shengyao Zhuang; Simon Gibson; Yash Dang; David Lawrence; Guido Zuccon (Int. J. Digit. Libr., 2024)

VISA: Retrieval Augmented Generation with Visual Source Attribution. - Xueguang Ma; Shengyao Zhuang; Bevan Koopman; Guido Zuccon; Wenhu Chen; Jimmy Lin (ACL (1), 2025)

The Impact of Auxiliary Patient Data on Automated Chest X-Ray Report Generation and How to Incorporate It. - Aaron Nicolson; Shengyao Zhuang; Jason Dowling; Bevan Koopman (ACL (1), 2025)

Corpus Subsampling: Estimating the Effectiveness of Neural Retrieval Models on Large Corpora. - Maik Fröbe; Andrew Parry; Harrisen Scells; Shuai Wang 032; Shengyao Zhuang; Guido Zuccon; Martin Potthast; Matthias Hagen (ECIR (1), 2025)

Set-Encoder: Permutation-Invariant Inter-passage Attention for Listwise Passage Re-ranking with Cross-Encoders. - Ferdinand Schlatt; Maik Fröbe; Harrisen Scells; Shengyao Zhuang; Bevan Koopman; Guido Zuccon; Benno Stein 001; Martin Potthast; Matthias Hagen (ECIR (2), 2025)

Rank-DistiLLM: Closing the Effectiveness Gap Between Cross-Encoders and LLMs for Passage Re-ranking. - Ferdinand Schlatt; Maik Fröbe; Harrisen Scells; Shengyao Zhuang; Bevan Koopman; Guido Zuccon; Benno Stein 001; Martin Potthast; Matthias Hagen (ECIR (3), 2025)

An Investigation of Prompt Variations for Zero-Shot LLM-Based Rankers. - Shuoqi Sun; Shengyao Zhuang; Shuai Wang 032; Guido Zuccon (ECIR (2), 2025)

2D Matryoshka Training for Information Retrieval. - Shuai Wang 032; Shengyao Zhuang; Bevan Koopman; Guido Zuccon (SIGIR, 2025)

Tevatron 2.0: Unified Document Retrieval Toolkit across Scale, Language, and Modality. - Xueguang Ma; Luyu Gao; Shengyao Zhuang; Jiaqi Samantha Zhan; Jamie Callan; Jimmy Lin (SIGIR, 2025)

Document Screenshot Retrievers are Vulnerable to Pixel Poisoning Attacks. - Shengyao Zhuang; Ekaterina Khramtsova; Xueguang Ma; Bevan Koopman; Jimmy Lin; Guido Zuccon (SIGIR, 2025)

R2LLMs: Retrieval and Ranking with LLMs. - Guido Zuccon; Shengyao Zhuang; Xueguang Ma (SIGIR, 2025)

ReSLLM: Large Language Models are Strong Resource Selectors for Federated Search. - Shuai Wang 032; Shengyao Zhuang; Bevan Koopman; Guido Zuccon (WWW (Companion Volume), 2025)

Rank-R1: Enhancing Reasoning in LLM-based Document Rerankers via Reinforcement Learning. - Shengyao Zhuang; Xueguang Ma; Bevan Koopman; Jimmy Lin; Guido Zuccon (CoRR, 2025)

LLM-VPRF: Large Language Model Based Vector Pseudo Relevance Feedback. - Hang Li 009; Shengyao Zhuang; Bevan Koopman; Guido Zuccon (CoRR, 2025)

MAGMaR Shared Task System Description: Video Retrieval with OmniEmbed. - Jiaqi Samantha Zhan; Crystina Zhang; Shengyao Zhuang; Xueguang Ma; Jimmy Lin (CoRR, 2025)

Leveraging Reference Documents for Zero-Shot Ranking via Large Language Models. - Jieran Li; Xiuyuan Hu; Yang Zhao; Shengyao Zhuang; Hao Zhang (CoRR, 2025)

Distillation versus Contrastive Learning: How to Train Your Rerankers. - Zhichao Xu 001; Zhiqi Huang; Shengyao Zhuang; Ashim Gupta; Vivek Srikumar (CoRR, 2025)

BrowseComp-Plus: A More Fair and Transparent Evaluation Benchmark of Deep-Research Agent. - Zijian Chen; Xueguang Ma; Shengyao Zhuang; Ping Nie; Kai Zou; Andrew Liu; Joshua Green; Kshama Patel; Ruoxi Meng; Mingyi Su; Sahel Sharifymoghaddam; Yanxi Li; et al. (CoRR, 2025)