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2025 Other Outputs Pseudo relevance feedback is enough to close the gap between small and large dense retrieval modelsLi, Hang, Wang, Xiao, Koopman, Bevan and Zuccon, Guido (2025). Pseudo relevance feedback is enough to close the gap between small and large dense retrieval models. doi: 10.48550/arXiv.2503.14887 |
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2025 Journal Article Report from the 4th Strategic Workshop on Information Retrieval in Lorne (SWIRL 2025)Trippas, Johanne R., Culpepper, J. Shane, Aliannejadi, Mohammad, Allan, James, Amigó, Enrique, Arguello, Jaime, Azzopardi, Leif, Bailey, Peter, Callan, Jamie, Capra, Rob, Craswell, Nick, Croft, Bruce, Dalton, Jeff, Demartini, Gianluca, Dietz, Laura, Dou, Zhicheng, Eickhoff, Carsten, Ekstrand, Michael, Ferro, Nicola, Fuhr, Norbert, Glowacka, Dorota, Hasibi, Faegheh, Hettiachchi, Danula, Jones, Rosie, Kamps, Jaap, Kando, Noriko, Karimi, Sarvnaz, Kato, Makoto P., Koopman, Bevan ... Zuccon, Guido (2025). Report from the 4th Strategic Workshop on Information Retrieval in Lorne (SWIRL 2025). ACM SIGIR Forum, 59 (1), 1-68. doi: 10.1145/3769733.3769739 |
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2025 Conference Publication ReSLLM: large language models are strong resource selectors for federated searchWang, Shuai, Zhuang, Shengyao, Koopman, Bevan and Zuccon, Guido (2025). ReSLLM: large language models are strong resource selectors for federated search. The ACM Web Conference 2025, Sydney, NSW Australia, 28 April-2 May 2025. New York, NY United States: Association for Computing Machinery. doi: 10.1145/3701716.3715595 |
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2025 Conference Publication An investigation of prompt variations for zero-shot LLM-based rankersSun, Shuoqi, Zhuang, Shengyao, Wang, Shuai and Zuccon, Guido (2025). An investigation of prompt variations for zero-shot LLM-based rankers. 47th European Conference on Information Retrieval, Lucca, Italy, 6-10 April 2025. Cham, Switzerland: Springer Cham. doi: 10.1007/978-3-031-88711-6_12 |
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2025 Conference Publication DenseReviewer: A Screening Prioritisation Tool for Systematic Review Based on Dense RetrievalMao, 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 |
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2025 Conference Publication Corpus subsampling: estimating the effectiveness of neural retrieval models on large corporaFröbe, Maik, Parry, Andrew, Scells, Harrisen, Wang, Shuai, Zhuang, Shengyao, Zuccon, Guido, Potthast, Martin and Hagen, Matthias (2025). Corpus subsampling: estimating the effectiveness of neural retrieval models on large corpora. 47th European Conference on Information Retrieval, Lucca, Italy, 6-10 April 2025. Cham, Switzerland: Springer Cham. doi: 10.1007/978-3-031-88708-6_29 |
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2025 Conference Publication Set-Encoder: Permutation-Invariant Inter-passage Attention for Listwise Passage Re-ranking with Cross-EncodersSchlatt, 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 |
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2025 Other Outputs LLM-VPRF: Large language model based vector pseudo relevance feedbackLi, 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 |
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2025 Conference Publication VISA: Retrieval Augmented Generation with Visual Source AttributionMa, Xueguang, Zhuang, Shengyao, Koopman, Bevan, Zuccon, Guido, Chen, Wenhu and Lin, Jimmy (2025). VISA: Retrieval Augmented Generation with Visual Source Attribution. 63rd Association for Computational Linguistics Meeting-ACL-Annual, Vienna Austria, Jul 27-Aug 01, 2025. STROUDSBURG: Association for Computational Linguistics (ACL). |
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2025 Conference Publication Rank-DistiLLM: Closing the Effectiveness Gap Between Cross-Encoders and LLMs for Passage Re-rankingSchlatt, 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 |
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2024 Conference Publication Understanding and Mitigating the Threat of Vec2Text to Dense Retrieval SystemsZhuang, 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 |
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2024 Conference Publication Searching in Professional Instant Messaging Applications: User Behaviour, Intent, and Pain-pointsSabei, 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 |
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2024 Other Outputs TPRF: A transformer-based pseudo-relevance feedback model for efficient and effective retrievalLi, 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 |
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2024 Conference Publication PromptReps: prompting large language models to generate dense and sparse representations for zero-shot document retrievalZhuang, 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 |
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2024 Conference Publication Source-Free Domain-Invariant Performance PredictionKhramtsova, 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 |
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2024 Conference Publication A setwise approach for effective and highly efficient zero-shot ranking with large language modelsZhuang, 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 |
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2024 Conference Publication Evaluating generative ad hoc information retrievalGienapp, 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 |
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2024 Conference Publication Embark on DenseQuest: a system for selecting the best dense retriever for a custom collectionKhramtsova, 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 |
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2024 Conference Publication Large language models based stemming for information retrieval: promises, pitfalls and failuresWang, 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 |
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2024 Conference Publication Revisiting document expansion and filtering for effective first-stage retrievalMansour, 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 |
