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ORCIDShengyao Zhuang
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- affiliation: University of Queensland, Brisbane, Queensland, Australia
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2020 – today
- 2024
[c30]Shuai Wang
, Harrisen Scells, Shengyao Zhuang, Martin Potthast, Bevan Koopman, Guido Zuccon:
Zero-Shot Generative Large Language Models for Systematic Review Screening Automation. ECIR (1) 2024: 403-420- [c29]Shengyao Zhuang, Honglei Zhuang, Bevan Koopman, Guido Zuccon:
A Setwise Approach for Effective and Highly Efficient Zero-shot Ranking with Large Language Models. SIGIR 2024: 38-47 - [c28]Watheq Mansour, Shengyao Zhuang, Guido Zuccon, Joel Mackenzie:
Revisiting Document Expansion and Filtering for Effective First-Stage Retrieval. SIGIR 2024: 186-196 - [c27]Shuai Wang, Ekaterina Khramtsova, Shengyao Zhuang, Guido Zuccon:
FeB4RAG: Evaluating Federated Search in the Context of Retrieval Augmented Generation. SIGIR 2024: 763-773 - [c26]Ekaterina Khramtsova, Shengyao Zhuang, Mahsa Baktashmotlagh, Guido Zuccon:
Leveraging LLMs for Unsupervised Dense Retriever Ranking. SIGIR 2024: 1307-1317 - [c25]Xinyu Mao, Shengyao Zhuang, Bevan Koopman, Guido Zuccon:
Dense Retrieval with Continuous Explicit Feedback for Systematic Review Screening Prioritisation. SIGIR 2024: 2357-2362 - [c24]Shuai Wang, Shengyao Zhuang, Guido Zuccon:
Large Language Models Based Stemming for Information Retrieval: Promises, Pitfalls and Failures. SIGIR 2024: 2492-2496 - [c23]Ekaterina Khramtsova, Teerapong Leelanupab, Shengyao Zhuang, Mahsa Baktashmotlagh, Guido Zuccon:
Embark on DenseQuest: A System for Selecting the Best Dense Retriever for a Custom Collection. SIGIR 2024: 2739-2743 - [i30]Shengyao Zhuang, Bevan Koopman, Guido Zuccon:
Team IELAB at TREC Clinical Trial Track 2023: Enhancing Clinical Trial Retrieval with Neural Rankers and Large Language Models. CoRR abs/2401.01566 (2024) - [i29]Shuai Wang, Harrisen Scells, Shengyao Zhuang, Martin Potthast, Bevan Koopman, Guido Zuccon:
Zero-shot Generative Large Language Models for Systematic Review Screening Automation. CoRR abs/2401.06320 (2024) - [i28]Shuai Wang, Shengyao Zhuang, Bevan Koopman, Guido Zuccon:
ReSLLM: Large Language Models are Strong Resource Selectors for Federated Search. CoRR abs/2401.17645 (2024) - [i27]Ekaterina Khramtsova, Shengyao Zhuang, Mahsa Baktashmotlagh, Guido Zuccon:
Leveraging LLMs for Unsupervised Dense Retriever Ranking. CoRR abs/2402.04853 (2024) - [i26]Shuai Wang, Shengyao Zhuang, Guido Zuccon:
Large Language Models for Stemming: Promises, Pitfalls and Failures. CoRR abs/2402.11757 (2024) - [i25]Shuai Wang, Ekaterina Khramtsova, Shengyao Zhuang, Guido Zuccon:
FeB4RAG: Evaluating Federated Search in the Context of Retrieval Augmented Generation. CoRR abs/2402.11891 (2024) - [i24]Shengyao Zhuang, Bevan Koopman, Xiaoran Chu, Guido Zuccon:
Understanding and Mitigating the Threat of Vec2Text to Dense Retrieval Systems. CoRR abs/2402.12784 (2024) - [i23]Ferdinand Schlatt, Maik Fröbe, Harrisen Scells, Shengyao Zhuang, Bevan Koopman, Guido Zuccon, Benno Stein, Martin Potthast, Matthias Hagen:
Set-Encoder: Permutation-Invariant Inter-Passage Attention for Listwise Passage Re-Ranking with Cross-Encoders. CoRR abs/2404.06912 (2024) - [i22]Shengyao Zhuang, Xueguang Ma, Bevan Koopman, Jimmy Lin, Guido Zuccon:
PromptReps: Prompting Large Language Models to Generate Dense and Sparse Representations for Zero-Shot Document Retrieval. CoRR abs/2404.18424 (2024) - [i21]Ferdinand Schlatt, Maik Fröbe, Harrisen Scells, Shengyao Zhuang, Bevan Koopman, Guido Zuccon, Benno Stein, Martin Potthast, Matthias Hagen:
A Systematic Investigation of Distilling Large Language Models into Cross-Encoders for Passage Re-ranking. CoRR abs/2405.07920 (2024) - [i20]Aaron Nicolson, Shengyao Zhuang, Jason Dowling, Bevan Koopman:
The Impact of Auxiliary Patient Data on Automated Chest X-Ray Report Generation and How to Incorporate It. CoRR abs/2406.13181 (2024) - [i19]Shuoqi Sun, Shengyao Zhuang, Shuai Wang, Guido Zuccon:
An Investigation of Prompt Variations for Zero-shot LLM-based Rankers. CoRR abs/2406.14117 (2024) - 2023
- [j2]Hang Li, Ahmed Mourad, Shengyao Zhuang, Bevan Koopman, Guido Zuccon:
Pseudo Relevance Feedback with Deep Language Models and Dense Retrievers: Successes and Pitfalls. ACM Trans. Inf. Syst. 41(3): 62:1-62:40 (2023) - [c22]Shengyao Zhuang, Bing Liu, Bevan Koopman, Guido Zuccon:
Open-source Large Language Models are Strong Zero-shot Query Likelihood Models for Document Ranking. EMNLP (Findings) 2023: 8807-8817 - [c21]Joel Mackenzie, Shengyao Zhuang, Guido Zuccon:
Exploring the Representation Power of SPLADE Models. ICTIR 2023: 143-147 - [c20]Guido Zuccon, Harrisen Scells, Shengyao Zhuang:
Beyond CO2 Emissions: The Overlooked Impact of Water Consumption of Information Retrieval Models. ICTIR 2023: 283-289 - [c19]Shengyao Zhuang, Linjun Shou, Guido Zuccon:
Augmenting Passage Representations with Query Generation for Enhanced Cross-Lingual Dense Retrieval. SIGIR 2023: 1827-1832 - [c18]Shengyao Zhuang, Linjun Shou, Jian Pei, Ming Gong, Houxing Ren, Guido Zuccon, Daxin Jiang:
Typos-aware Bottlenecked Pre-Training for Robust Dense Retrieval. SIGIR-AP 2023: 212-222 - [c17]Ekaterina Khramtsova, Shengyao Zhuang, Mahsa Baktashmotlagh, Xi Wang, Guido Zuccon:
Selecting which Dense Retriever to use for Zero-Shot Search. SIGIR-AP 2023: 223-233 - [i18]Shengyao Zhuang, Linjun Shou, Jian Pei, Ming Gong, Houxing Ren, Guido Zuccon, Daxin Jiang:
Typos-aware Bottlenecked Pre-Training for Robust Dense Retrieval. CoRR abs/2304.08138 (2023) - [i17]Shengyao Zhuang, Linjun Shou, Guido Zuccon:
Augmenting Passage Representations with Query Generation for Enhanced Cross-Lingual Dense Retrieval. CoRR abs/2305.03950 (2023) - [i16]Guido Zuccon, Harrisen Scells, Shengyao Zhuang:
Beyond CO2 Emissions: The Overlooked Impact of Water Consumption of Information Retrieval Models. CoRR abs/2306.16668 (2023) - [i15]Joel Mackenzie, Shengyao Zhuang, Guido Zuccon:
Exploring the Representation Power of SPLADE Models. CoRR abs/2306.16680 (2023) - [i14]Ekaterina Khramtsova, Shengyao Zhuang, Mahsa Baktashmotlagh, Xi Wang, Guido Zuccon:
Selecting which Dense Retriever to use for Zero-Shot Search. CoRR abs/2309.09403 (2023) - [i13]Shengyao Zhuang, Honglei Zhuang, Bevan Koopman, Guido Zuccon:
A Setwise Approach for Effective and Highly Efficient Zero-shot Ranking with Large Language Models. CoRR abs/2310.09497 (2023) - [i12]Shengyao Zhuang, Bing Liu, Bevan Koopman, Guido Zuccon:
Open-source Large Language Models are Strong Zero-shot Query Likelihood Models for Document Ranking. CoRR abs/2310.13243 (2023) - 2022
- [j1]Shengyao Zhuang, Zhihao Qiao, Guido Zuccon:
Reinforcement online learning to rank with unbiased reward shaping. Inf. Retr. J. 25(4): 386-413 (2022) - [c16]Hang Li, Shengyao Zhuang, Xueguang Ma, Jimmy Lin, Guido Zuccon:
Pseudo-Relevance Feedback with Dense Retrievers in Pyserini. ADCS 2022: 1:1-1:6 - [c15]Shengyao Zhuang, Xinyu Mao, Guido Zuccon:
Robustness of Neural Rankers to Typos: A Comparative Study. ADCS 2022: 6:1-6:6 - [c14]Hang Li, Shengyao Zhuang, Ahmed Mourad, Xueguang Ma, Jimmy Lin, Guido Zuccon:
Improving Query Representations for Dense Retrieval with Pseudo Relevance Feedback: A Reproducibility Study. ECIR (1) 2022: 599-612 - [c13]Shengyao Zhuang, Hang Li, Guido Zuccon:
Implicit Feedback for Dense Passage Retrieval: A Counterfactual Approach. SIGIR 2022: 18-28 - [c12]Shengyao Zhuang, Guido Zuccon:
CharacterBERT and Self-Teaching for Improving the Robustness of Dense Retrievers on Queries with Typos. SIGIR 2022: 1444-1454 - [c11]Hang Li, Shuai Wang, Shengyao Zhuang, Ahmed Mourad, Xueguang Ma, Jimmy Lin, Guido Zuccon:
To Interpolate or not to Interpolate: PRF, Dense and Sparse Retrievers. SIGIR 2022: 2495-2500 - [c10]Harrisen Scells, Shengyao Zhuang, Guido Zuccon:
Reduce, Reuse, Recycle: Green Information Retrieval Research. SIGIR 2022: 2825-2837 - [c9]Shengyao Zhuang, Guido Zuccon:
Asyncval: A Toolkit for Asynchronously Validating Dense Retriever Checkpoints During Training. SIGIR 2022: 3235-3239 - [i11]Shengyao Zhuang, Zhihao Qiao, Guido Zuccon:
Reinforcement Online Learning to Rank with Unbiased Reward Shaping. CoRR abs/2201.01534 (2022) - [i10]Shengyao Zhuang, Guido Zuccon:
Asyncval: A Toolkit for Asynchronously Validating Dense Retriever Checkpoints during Training. CoRR abs/2202.12510 (2022) - [i9]Shengyao Zhuang, Guido Zuccon:
CharacterBERT and Self-Teaching for Improving the Robustness of Dense Retrievers on Queries with Typos. CoRR abs/2204.00716 (2022) - [i8]Shengyao Zhuang, Hang Li, Guido Zuccon:
Implicit Feedback for Dense Passage Retrieval: A Counterfactual Approach. CoRR abs/2204.00718 (2022) - [i7]Hang Li, Shuai Wang, Shengyao Zhuang, Ahmed Mourad, Xueguang Ma, Jimmy Lin, Guido Zuccon:
To Interpolate or not to Interpolate: PRF, Dense and Sparse Retrievers. CoRR abs/2205.00235 (2022) - [i6]Shengyao Zhuang, Houxing Ren, Linjun Shou, Jian Pei, Ming Gong, Guido Zuccon, Daxin Jiang:
Bridging the Gap Between Indexing and Retrieval for Differentiable Search Index with Query Generation. CoRR abs/2206.10128 (2022) - [i5]Bevan Koopman, Ahmed Mourad, Hang Li, Anton van der Vegt, Shengyao Zhuang, Simon Gibson, Yash Dang, David Lawrence, Guido Zuccon:
AgAsk: An Agent to Help Answer Farmer's Questions From Scientific Documents. CoRR abs/2212.10762 (2022) - 2021
- [c8]Shuyi Wang, Shengyao Zhuang, Guido Zuccon:
Federated Online Learning to Rank with Evolution Strategies: A Reproducibility Study. ECIR (2) 2021: 134-149 - [c7]Shengyao Zhuang, Hang Li, Guido Zuccon:
Deep Query Likelihood Model for Information Retrieval. ECIR (2) 2021: 463-470 - [c6]Shengyao Zhuang, Guido Zuccon:
Dealing with Typos for BERT-based Passage Retrieval and Ranking. EMNLP (1) 2021: 2836-2842 - [c5]Shuyi Wang, Bing Liu, Shengyao Zhuang, Guido Zuccon:
Effective and Privacy-preserving Federated Online Learning to Rank. ICTIR 2021: 3-12 - [c4]Shuai Wang, Shengyao Zhuang, Guido Zuccon:
BERT-based Dense Retrievers Require Interpolation with BM25 for Effective Passage Retrieval. ICTIR 2021: 317-324 - [c3]Shengyao Zhuang, Guido Zuccon:
How do Online Learning to Rank Methods Adapt to Changes of Intent? SIGIR 2021: 911-920 - [c2]Shengyao Zhuang, Guido Zuccon:
TILDE: Term Independent Likelihood moDEl for Passage Re-ranking. SIGIR 2021: 1483-1492 - [i4]Shengyao Zhuang, Guido Zuccon:
Fast Passage Re-ranking with Contextualized Exact Term Matching and Efficient Passage Expansion. CoRR abs/2108.08513 (2021) - [i3]Hang Li, Ahmed Mourad, Shengyao Zhuang, Bevan Koopman, Guido Zuccon:
Pseudo Relevance Feedback with Deep Language Models and Dense Retrievers: Successes and Pitfalls. CoRR abs/2108.11044 (2021) - [i2]Shengyao Zhuang, Guido Zuccon:
Dealing with Typos for BERT-based Passage Retrieval and Ranking. CoRR abs/2108.12139 (2021) - [i1]Hang Li, Shengyao Zhuang, Ahmed Mourad, Xueguang Ma, Jimmy Lin, Guido Zuccon:
Improving Query Representations for Dense Retrieval with Pseudo Relevance Feedback: A Reproducibility Study. CoRR abs/2112.06400 (2021) - 2020
- [c1]Shengyao Zhuang, Guido Zuccon:
Counterfactual Online Learning to Rank. ECIR (1) 2020: 415-430
Coauthor Index
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