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2025

Conference Publication

Set-Encoder: Permutation-Invariant Inter-passage Attention for Listwise Passage Re-ranking with Cross-Encoders

Schlatt, Ferdinand, Fröbe, Maik, Scells, Harrisen, Zhuang, Shengyao, Koopman, Bevan, Zuccon, Guido, Stein, Benno, Potthast, Martin and Hagen, Matthias (2025). Set-Encoder: Permutation-Invariant Inter-passage Attention for Listwise Passage Re-ranking with Cross-Encoders. 47th European Conference on Information Retrieval, ECIR 2025, Lucca, Italy, 6-10 April 2025. Cham, Switzerland: Springer. doi: 10.1007/978-3-031-88711-6_1

Set-Encoder: Permutation-Invariant Inter-passage Attention for Listwise Passage Re-ranking with Cross-Encoders

2025

Conference Publication

DenseReviewer: A Screening Prioritisation Tool for Systematic Review Based on Dense Retrieval

Mao, Xinyu, Leelanupab, Teerapong, Scells, Harrisen and Zuccon, Guido (2025). DenseReviewer: A Screening Prioritisation Tool for Systematic Review Based on Dense Retrieval. 47th European Conference on Information Retrieval, ECIR 2025, Lucca, Italy, 6-10 April 2025. Cham, Switzerland: Springer. doi: 10.1007/978-3-031-88720-8_11

DenseReviewer: A Screening Prioritisation Tool for Systematic Review Based on Dense Retrieval

2025

Other Outputs

LLM-VPRF: Large language model based vector pseudo relevance feedback

Li, Hang, Zhuang, Shengyao, Koopman, Bevan and Zuccon, Guido (2025). LLM-VPRF: Large language model based vector pseudo relevance feedback. doi: 10.48550/arXiv.2504.01448

LLM-VPRF: Large language model based vector pseudo relevance feedback

2025

Conference Publication

Rank-DistiLLM: Closing the Effectiveness Gap Between Cross-Encoders and LLMs for Passage Re-ranking

Schlatt, Ferdinand, Fröbe, Maik, Scells, Harrisen, Zhuang, Shengyao, Koopman, Bevan, Zuccon, Guido, Stein, Benno, Potthast, Martin and Hagen, Matthias (2025). Rank-DistiLLM: Closing the Effectiveness Gap Between Cross-Encoders and LLMs for Passage Re-ranking. 47th European Conference on Information Retrieval, ECIR 2025, Lucca, Italy, 6-10 April 2025. Cham, Switzerland: Springer. doi: 10.1007/978-3-031-88714-7_31

Rank-DistiLLM: Closing the Effectiveness Gap Between Cross-Encoders and LLMs for Passage Re-ranking

2024

Conference Publication

Searching in Professional Instant Messaging Applications: User Behaviour, Intent, and Pain-points

Sabei, Ismail, Galal, Mahmoud, Koopman, Bevan and Zuccon, Guido (2024). Searching in Professional Instant Messaging Applications: User Behaviour, Intent, and Pain-points. 2024 Annual International ACM SIGIR Conference on Research and Development in Information Retrieval in the Asia Pacific Region, Tokyo, Japan, 9-12 December 2024. New York, NY United States: Association for Computing Machinery. doi: 10.1145/3673791.3698417

Searching in Professional Instant Messaging Applications: User Behaviour, Intent, and Pain-points

2024

Conference Publication

Understanding and Mitigating the Threat of Vec2Text to Dense Retrieval Systems

Zhuang, Shengyao, Koopman, Bevan, Chu, Xiaoran and Zuccon, Guido (2024). Understanding and Mitigating the Threat of Vec2Text to Dense Retrieval Systems. 2024 Annual International ACM SIGIR Conference on Research and Development in Information Retrieval in the Asia Pacific Region, Tokyo, Japan, 9-12 December 2024. New York, NY United States: Association for Computing Machinery. doi: 10.1145/3673791.3698414

Understanding and Mitigating the Threat of Vec2Text to Dense Retrieval Systems

2024

Other Outputs

TPRF: A transformer-based pseudo-relevance feedback model for efficient and effective retrieval

Li, Hang, Yu, Chuting, Mourad, Ahmed, Koopman, Bevan and Zuccon, Guido (2024). TPRF: A transformer-based pseudo-relevance feedback model for efficient and effective retrieval. doi: 10.48550/arXiv.2401.13509

TPRF: A transformer-based pseudo-relevance feedback model for efficient and effective retrieval

2024

Conference Publication

PromptReps: prompting large language models to generate dense and sparse representations for zero-shot document retrieval

Zhuang, Shengyao, Ma, Xueguang, Koopman, Bevan, Lin, Jimmy and Zuccon, Guido (2024). PromptReps: prompting large language models to generate dense and sparse representations for zero-shot document retrieval. 29th Conference on Empirical Methods in Natural Language Processing, Miami, FL USA, 12-16 November 2024. Stroudsberg, PA USA: Association for Computational Linguistics. doi: 10.18653/v1/2024.emnlp-main.250

PromptReps: prompting large language models to generate dense and sparse representations for zero-shot document retrieval

2024

Conference Publication

Source-Free Domain-Invariant Performance Prediction

Khramtsova, Ekaterina, Baktashmotlagh, Mahsa, Zuccon, Guido, Wang, Xi and Salzmann, Mathieu (2024). Source-Free Domain-Invariant Performance Prediction. 18th European Conference on Computer Vision ECCV 2024, Milan, Italy, 29 September – 4 October 2024. Cham, Switzerland: Springer. doi: 10.1007/978-3-031-72989-8_6

Source-Free Domain-Invariant Performance Prediction

2024

Conference Publication

Embark on DenseQuest: a system for selecting the best dense retriever for a custom collection

Khramtsova, Ekaterina, Leelanupab, Teerapong, Zhuang, Shengyao, Baktashmotlagh, Mahsa and Zuccon, Guido (2024). Embark on DenseQuest: a system for selecting the best dense retriever for a custom collection. SIGIR '24: 47th International ACM SIGIR Conference on Research and Development in Information Retrieval, Washington, DC, United States, 14-18 July 2024. New York, NY, United States: ACM. doi: 10.1145/3626772.3657674

Embark on DenseQuest: a system for selecting the best dense retriever for a custom collection

2024

Conference Publication

Large language models based stemming for information retrieval: promises, pitfalls and failures

Wang, Shuai, Zhuang, Shengyao and Zuccon, Guido (2024). Large language models based stemming for information retrieval: promises, pitfalls and failures. SIGIR '24: 47th International ACM SIGIR Conference on Research and Development in Information Retrieval, Washington, DC, United States, 14-18 July 2024. New York, NY, United States: ACM. doi: 10.1145/3626772.3657949

Large language models based stemming for information retrieval: promises, pitfalls and failures

2024

Conference Publication

Revisiting document expansion and filtering for effective first-stage retrieval

Mansour, Watheq, Zhuang, Shengyao, Zuccon, Guido and Mackenzie, Joel (2024). Revisiting document expansion and filtering for effective first-stage retrieval. SIGIR '24, Washington, DC, United States, 14-18 July 2024. New York, NY, United States: ACM. doi: 10.1145/3626772.3657850

Revisiting document expansion and filtering for effective first-stage retrieval

2024

Journal Article

ACM SIGIR 2024 Chairs' Welcome

Hui Yang, Grace, Wang, Hongning, Han, Sam, Hauff, Claudia, Zuccon, Guido and Zhang, Yi (2024). ACM SIGIR 2024 Chairs' Welcome. SIGIR 2024 - Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval, iii-v. doi: 10.1145/3626772

ACM SIGIR 2024 Chairs' Welcome

2024

Conference Publication

Leveraging LLMs for unsupervised dense retriever ranking

Khramtsova, Ekaterina, Zhuang, Shengyao, Baktashmotlagh, Mahsa and Zuccon, Guido (2024). Leveraging LLMs for unsupervised dense retriever ranking. SIGIR '24: 47th International ACM SIGIR Conference on Research and Development in Information Retrieval, Washington, DC, United States, 14-18 July 2024. New York, NY, United States: ACM. doi: 10.1145/3626772.3657798

Leveraging LLMs for unsupervised dense retriever ranking

2024

Conference Publication

Dense retrieval with continuous explicit feedback for systematic review screening prioritisation

Mao, Xinyu, Zhuang, Shengyao, Koopman, Bevan and Zuccon, Guido (2024). Dense retrieval with continuous explicit feedback for systematic review screening prioritisation. SIGIR '24: 47th International ACM SIGIR Conference on Research and Development in Information Retrieval, Washington, DC, United States, 14-18 July 2024. New York, NY, United States: ACM. doi: 10.1145/3626772.3657921

Dense retrieval with continuous explicit feedback for systematic review screening prioritisation

2024

Conference Publication

FeB4RAG: evaluating federated search in the context of retrieval augmented generation

Wang, Shuai, Khramtsova, Ekaterina, Zhuang, Shengyao and Zuccon, Guido (2024). FeB4RAG: evaluating federated search in the context of retrieval augmented generation. SIGIR '24: 47th International ACM SIGIR Conference on Research and Development in Information Retrieval, Washington, DC, United States, 14-18 July 2024. New York, NY, United States: ACM. doi: 10.1145/3626772.3657853

FeB4RAG: evaluating federated search in the context of retrieval augmented generation

2024

Conference Publication

A setwise approach for effective and highly efficient zero-shot ranking with large language models

Zhuang, Shengyao, Zhuang, Honglei, Koopman, Bevan and Zuccon, Guido (2024). A setwise approach for effective and highly efficient zero-shot ranking with large language models. SIGIR ’24, Washington, DC, United States, 14-18 July 2024. New York, NY, United States: ACM. doi: 10.1145/3626772.3657813

A setwise approach for effective and highly efficient zero-shot ranking with large language models

2024

Conference Publication

Evaluating generative ad hoc information retrieval

Gienapp, Lukas, Scells, Harrisen, Deckers, Niklas, Bevendorff, Janek, Wang, Shuai, Kiesel, Johannes, Syed, Shahbaz, Fröbe, Maik, Zuccon, Guido, Stein, Benno, Hagen, Matthias and Potthast, Martin (2024). Evaluating generative ad hoc information retrieval. SIGIR '24: 47th International ACM SIGIR Conference on Research and Development in Information Retrieva, Washington, DC, United States, 14-18 July 2024. New York, NY, United States: ACM. doi: 10.1145/3626772.3657849

Evaluating generative ad hoc information retrieval

2024

Journal Article

The new paradigm in machine learning – foundation models, large language models and beyond: a primer for physicians

Scott, Ian A. and Zuccon, Guido (2024). The new paradigm in machine learning – foundation models, large language models and beyond: a primer for physicians. Internal Medicine Journal, 54 (5), 705-715. doi: 10.1111/imj.16393

The new paradigm in machine learning – foundation models, large language models and beyond: a primer for physicians

2024

Conference Publication

CoLAL: Co-learning active learning for text classification

Le, Linh, Zhao, Genghong, Zhang, Xia, Zuccon, Guido and Demartini, Gianluca (2024). CoLAL: Co-learning active learning for text classification. Thirty-Eighth AAAI Conference on Artificial Intelligence, Vancouver, BC Canada, 20–27 February 2024. Washington, DC United States: Association for the Advancement of Artificial Intelligence. doi: 10.1609/aaai.v38i12.29235

CoLAL: Co-learning active learning for text classification