| BERT-based Dense Retrievers Require Interpolation with BM25 for Effective Passage Retrieval S Wang, S Zhuang, G Zuccon In Proceedings of the 2021 ACM SIGIR International Conference on the Theory …, 2021 | 84 | 2021 |
| TILDE: Term Independent Likelihood moDEl for passage re-ranking S Zhuang, G Zuccon In Proceedings of the 44th International ACM SIGIR Conference on Research …, 2021 | 83 | 2021 |
| Reduce, reuse, recycle: Green information retrieval research H Scells, S Zhuang, G Zuccon Proceedings of the 45th International ACM SIGIR Conference on Research and …, 2022 | 77 | 2022 |
| Bridging the gap between indexing and retrieval for differentiable search index with query generation S Zhuang, H Ren, L Shou, J Pei, M Gong, G Zuccon, D Jiang arXiv preprint arXiv:2206.10128, 2022 | 76 | 2022 |
| Fast Passage Re-ranking with Contextualized Exact Term Matching and Efficient Passage Expansion S Zhuang, G Zuccon arXiv preprint arXiv:2108.08513, 2021 | 73 | 2021 |
| Dealing with Typos for BERT-based Passage Retrieval and Ranking S Zhuang, G Zuccon Proceedings of the 2021 Conference on Empirical Methods in Natural Language …, 2021 | 52 | 2021 |
| A Setwise Approach for Effective and Highly Efficient Zero-shot Ranking with Large Language Models S Zhuang, H Zhuang, B Koopman, G Zuccon Proceedings of the 47th International ACM SIGIR Conference on Research and …, 2024 | 51 | 2024 |
| Pseudo relevance feedback with deep language models and dense retrievers: Successes and pitfalls H Li, A Mourad, S Zhuang, B Koopman, G Zuccon ACM Transactions on Information Systems 41 (3), 1-40, 2023 | 49 | 2023 |
| Open-source Large Language Models are Strong Zero-shot Query Likelihood Models for Document Ranking S Zhuang, B Liu, B Koopman, G Zuccon Findings of the Association for Computational Linguistics: EMNLP 2023, 2023 | 42 | 2023 |
| CharacterBERT and Self-Teaching for Improving the Robustness of Dense Retrievers on Queries with Typos S Zhuang, G Zuccon In Proceedings of the 45th International ACM SIGIR Conference on Research …, 2022 | 40 | 2022 |
| Deep Query Likelihood Model for Information Retrieval S Zhuang, H Li, G Zuccon European Conference on Information Retrieval, 2021 | 35 | 2021 |
| Improving Query Representations for Dense Retrieval with Pseudo Relevance Feedback: A Reproducibility Study H Li, S Zhuang, A Mourad, X Ma, J Lin, G Zuccon European Conference on Information Retrieval, 2022 | 30 | 2022 |
| To Interpolate or not to Interpolate: PRF, Dense and Sparse Retrievers H Li, S Wang, S Zhuang, A Mourad, X Ma, J Lin, G Zuccon In Proceedings of the 45th International ACM SIGIR Conference on Research …, 2022 | 29 | 2022 |
| Counterfactual Online Learning to Rank S Zhuang, G Zuccon European Conference on Information Retrieval, 2020 | 23 | 2020 |
| Beyond CO2 emissions: The overlooked impact of water consumption of information retrieval models G Zuccon, H Scells, S Zhuang Proceedings of the 2023 ACM SIGIR International Conference on Theory of …, 2023 | 17 | 2023 |
| Effective and Privacy-preserving Federated Online Learning to Rank S Wang, B Liu, S Zhuang, G Zuccon In Proceedings of the 2021 ACM SIGIR International Conference on the Theory …, 2021 | 15 | 2021 |
| PromptReps: Prompting Large Language Models to Generate Dense and Sparse Representations for Zero-Shot Document Retrieval S Zhuang, X Ma, B Koopman, J Lin, G Zuccon arXiv preprint arXiv:2404.18424, 2024 | 14 | 2024 |
| Federated Online Learning to Rank with Evolution Strategies: A Reproducibility Study S Wang, S Zhuang, G Zuccon European Conference on Information Retrieval, 134-149, 2021 | 13 | 2021 |
| Leveraging LLMs for Unsupervised Dense Retriever Ranking E Khramtsova*, S Zhuang*, M Baktashmotlagh, G Zuccon Proceedings of the 47th International ACM SIGIR Conference on Research and …, 2024 | 12 | 2024 |
| Zero-shot Generative Large Language Models for Systematic Review Screening Automation S Wang, H Scells, S Zhuang, M Potthast, B Koopman, G Zuccon European Conference on Information Retrieval, 403-420, 2024 | 10 | 2024 |
Guido ZucconProfessor, University of Queensland; Director of AI, Queensland Digital Health Centre在 uq.edu.au 的电子邮件经过验证