I am a postdoctoral researcher at CSIRO, Australian e-Health Research Centre, where I focus on developing large language model-based search engine systems in the medical domain. Before joining CSIRO, I was a Ph.D. student at the ielab, EECS, The University of Queensland, Australia, supervised by Professor Guido Zuccon. My primary research interests lie in information retrieval, large language model-based neural rankers, and NLP in general.

Publications

2023

Teaching Pre-Trained Language Models to Rank Effectively, Efficiently, and Robustly

Shengyao Zhuang.

Published in The University of Queensland, 2023. Thesis

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Typos-aware Bottlenecked Pre-Training for Robust Dense Retrieval

Shengyao Zhuang, Linjun Shou, Jian Pei, Ming Gong, Houxing Ren, Guido Zuccon and Daxin Jiang.

Published in Proceedings of the 1st International ACM SIGIR Conference on Information Retrieval in the Asia Pacific (SIGIR-AP ’23), 2023. Full paper

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A Setwise Approach for Effective and Highly Efficient Zero-shot Ranking with Large Language Models

Shengyao Zhuang, Honglei Zhuang, Bevan Koopman and Guido Zuccon.

Published in Arxiv Preprint, 2023. Full paper

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Selecting which Dense Retriever to use for Zero-Shot Search

Ekaterina Khramtsova, Shengyao Zhuang, Mahsa Baktashmotlagh, Xi Wang and Guido Zuccon.

Published in Proceedings of the 1st International ACM SIGIR Conference on Information Retrieval in the Asia Pacific (SIGIR-AP ’23), 2023. Full paper

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Augmenting Passage Representations with Query Generation for Enhanced Cross-Lingual Dense Retrieval

Shengyao Zhuang, Linjun Shou, Guido Zuccon

Published in Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR ’23), 2023. Short paper

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Exploring the Representation Power of SPLADE Models

Joel Mackenziem, Shengyao Zhuang, Guido Zuccon

Published in Proceedings of the 2023 ACM SIGIR International Conference on the Theory of Information Retrieval (ICTIR ’23), 2023. Short paper

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Beyond CO2 Emissions: The Overlooked Impact of Water Consumption of Information Retrieval Models

Guido Zuccon, Harrisen Scells, Shengyao Zhuang

Published in Proceedings of the 2023 ACM SIGIR International Conference on the Theory of Information Retrieval (ICTIR ’23), 2023. Short paper

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Open-source Large Language Models are Strong Zero-shot Query Likelihood Models for Document Ranking

Shengyao Zhuang, Bing Liu, Bevan Koopman and Guido Zuccon.

Published in Findings of the Association for Computational Linguistics: EMNLP 2023, 2023. Short paper

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Bridging the Gap Between Indexing and Retrieval for Differentiable Search Index with Query Generation

Shengyao Zhuang, Houxing Ren, Linjun Shou, Jian Pei, Ming Gong, Guido Zuccon and Daxin Jiang.

Published in The First Workshop on Generative Information Retrieval at SIGIR2023, 2023. Full paper

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2022

To Interpolate or not to Interpolate: PRF, Dense and Sparse Retrievers

Hang Li, Shuai Wang, Shengyao Zhuang, Ahmed Mourad, Xueguang Ma, Jimmy Lin, Guido Zuccon

Published in Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR ’22), 2022. Short paper

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Reduce, Reuse, Recycle: Green Information Retrieval Research

Harry Scells, Shengyao Zhuang, Guido Zuccon ( Best Paper Honorable Mention Award )

Published in Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR ’22), 2022. Perspective paper

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Implicit Feedback for Dense Passage Retrieval: A Counterfactual Approach

Shengyao Zhuang, Hang Li and Guido Zuccon

Published in Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR ’22), 2022. Full paper

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CharacterBERT and Self-Teaching for Improving the Robustness of Dense Retrievers on Queries with Typos

Shengyao Zhuang, Guido Zuccon

Published in Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR ’22), 2022. Full paper

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Asyncval: A Toolkit for Asynchronously Validating Dense Retriever Checkpoints during Training

Shengyao Zhuang, Guido Zuccon

Published in Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR ’22), 2022. Demo paper

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Reinforcement Online Learning to Rank with Unbiased Reward Shaping

Shengyao Zhuang, Zhihao Qiao, Guido Zuccon

Published in Information Retrieval Journal (IRJ), 2022. Journal paper

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Improving Query Representations for Dense Retrieval with Pseudo Relevance Feedback: A Reproducibility Study

Hang Li, Shengyao Zhuang, Ahmed Mourad, Xueguang Ma, Jimmy Lin, Guido Zuccon

Published in Proceedings of the 44th European Conference on Information Retrieval (ECIR ’22), 2022. Reproducibility paper

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Pseudo-Relevance Feedback with Dense Retrievers in Pyserini

Hang Li, Shengyao Zhuang, Xueguang Ma, Jimmy Lin, Guido Zuccon

Published in Proceedings of the 26th Australasian Document Computing Symposium (ADCS ’22), 2022. Demo paper

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Robustness of Neural Rankers to Typos: A Comparative Study

Shengyao Zhuang, Xinyu Mao, Guido Zuccon ( Best Paper Award )

Published in Proceedings of the 26th Australasian Document Computing Symposium (ADCS ’22), 2022. Short paper

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Pseudo Relevance Feedback with Deep Language Models and Dense Retrievers: Successes and Pitfalls

Hang Li, Ahmed Mourad, Shengyao Zhuang, Bevan Koopman, Guido Zuccon

Published in Transactions on Information Systems (TOIS), 2022. Journal paper

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2021

Fast Passage Re-ranking with Contextualized Exact Term Matching and Efficient Passage Expansion

Shengyao Zhuang, Guido Zuccon

Published in arxiv preprint, 2021. Full paper

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TILDE: Term Independent Likelihood moDEl for Passage Re-ranking

Shengyao Zhuang, Guido Zuccon

Published in Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR ’21), 2021. Full paper

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How do Online Learning to Rank Methods Adapt to Changes of Intent?

Shengyao Zhuang, Guido Zuccon

Published in Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR ’21), 2021. Full paper

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BERT-based Dense Retrievers Require Interpolation with BM25 for Effective Passage Retrieval

Shuai Wang, Shengyao Zhuang, Guido Zuccon

Published in Proceedings of the 2021 ACM SIGIR International Conference on the Theory of Information Retrieval (ICTIR ’21), 2021. Full paper

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Effective and Privacy-preserving Federated Online Learning to Rank

Shuyi Wang, Bing Liu, Shengyao Zhuang, Guido Zuccon ( Best Student Paper Award )

Published in Proceedings of the 2021 ACM SIGIR International Conference on the Theory of Information Retrieval (ICTIR ’21), 2021. Full paper

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Dealing with Typos for BERT-based Passage Retrieval and Ranking

Shengyao Zhuang, Guido Zuccon

Published in In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2021. Short paper

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Federated Online Learning to Rank with Evolution Strategies: A Reproducibility Study

Shuyi Wang, Shengyao Zhuang, Guido Zuccon

Published in Advances in Information Retrieval - 43rd European Conference on IR Research, ECIR 2021, 2021. Reproducibility paper

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Deep Query Likelihood Model for Information Retrieval

Shengyao Zhuang, Hang Li, Guido Zuccon

Published in Advances in Information Retrieval - 43rd European Conference on IR Research, ECIR 2021, 2021. Short paper

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2020

Counterfactual Online Learning to Rank

Shengyao Zhuang, Guido Zuccon

Published in Advances in Information Retrieval - 42nd European Conference on IR Research, ECIR 2020, 2020. Full paper

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