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2025

Conference Publication

ReSLLM: large language models are strong resource selectors for federated search

Wang, 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 USA: Association for Computing Machinery. doi: 10.1145/3701716.3715595

ReSLLM: large language models are strong resource selectors for federated search

2025

Conference Publication

An investigation of prompt variations for zero-shot LLM-based rankers

Sun, 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

An investigation of prompt variations for zero-shot LLM-based rankers

2025

Conference Publication

Corpus subsampling: estimating the effectiveness of neural retrieval models on large corpora

Fröbe, 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

Corpus subsampling: estimating the effectiveness of neural retrieval models on large corpora

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

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

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

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

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

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

2024

Conference Publication

How to Forget Clients in Federated Online Learning to Rank?

Wang, Shuyi, Liu, Bing and Zuccon, Guido (2024). How to Forget Clients in Federated Online Learning to Rank?. 46th European Conference on Information Retrieval, ECIR 2024, Glasgow, Scotland, 24 - 28 March 2024. Cham, Switzerland: Springer. doi: 10.1007/978-3-031-56063-7_7

How to Forget Clients in Federated Online Learning to Rank?

2024

Conference Publication

Zero-shot generative large language models for systematic review screening automation

Wang, Shuai, Scells, Harrisen, Zhuang, Shengyao, Potthast, Martin, Koopman, Bevan and Zuccon, Guido (2024). Zero-shot generative large language models for systematic review screening automation. 46th European Conference on Information Retrieval, ECIR 2024, Glasgow, United Kingdom, 24-28 March 2024. Cham, Switzerland: Springer Nature Switzerland. doi: 10.1007/978-3-031-56027-9_25

Zero-shot generative large language models for systematic review screening automation

2024

Conference Publication

Stochastic Featurization for Active Learning

Le, Linh, Nguyen, Minh-Tien, Tran, Khai Phan, Zhao, Genghong, Xia, Zhang, Zuccon, Guido and Demartini, Gianluca (2024). Stochastic Featurization for Active Learning. Second International Workshop, TAI4H 2024, Jeju, South Korea, 4 August 2024. Cham, Switzerland: Springer Nature Switzerland. doi: 10.1007/978-3-031-67751-9_5

Stochastic Featurization for Active Learning

2023

Conference Publication

Selecting which dense retriever to use for zero-shot search

Khramtsova, Ekaterina, Zhuang, Shengyao, Baktashmotlagh, Mahsa, Wang, Xi and Zuccon, Guido (2023). Selecting which dense retriever to use for zero-shot search. SIGIR-AP 2023 - Annual International ACM SIGIR Conference on Research and Development in Information Retrieval in the Asia Pacific Region, Beijing, China, 26-28 November 2023. New York, United States: Association for Computing Machinery. doi: 10.1145/3624918.3625330

Selecting which dense retriever to use for zero-shot search