Shengyao (Arvin) Zhuang 庄胜尧

I am an Applied Scientist at Amazon’s AGI group, specializing in advancing web search technologies that power Amazon’s product ecosystem. My research focuses on information retrieval, large language model–based neural rankers, and natural language processing. Previously, I was a Postdoctoral Researcher at CSIRO’s Australian e-Health Research Centre, where I developed LLM-driven search systems for the medical domain. I earned my Ph.D. in Computer Science from the University of Queensland’s ielab, under the supervision of Professor Guido Zuccon.

Publications

2024

Large Language Models for Stemming: Promises, Pitfalls and Failures

Shuai Wang, Shengyao Zhuang, and Guido Zuccon.

Published in Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR ’24), 2024. Short paper

Leveraging LLMs for Unsupervised Dense Retriever Ranking

Ekaterina Khramtsova, Shengyao Zhuang (equal contribution), Mahsa Baktashmotlagh, and Guido Zuccon ()

Published in Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR ’24), 2024. Full paper

Understanding and Mitigating the Threat of Vec2Text to Dense Retrieval Systems

Shengyao Zhuang, Bevan Koopman, Xiaoran Chu, and Guido Zuccon.

Published in Proceedings of the 2st International ACM SIGIR Conference on Information Retrieval in the Asia Pacific (SIGIR-AP ’24), 2024, 2024. Full paper

Starbucks: Improved Training for 2D Matryoshka Embeddings

Shengyao Zhuang, Shuai Wang, Bevan Koopman, and Guido Zuccon.

Published in Arxiv, 2024. Full paper

PromptReps: Prompting Large Language Models to Generate Dense and Sparse Representations for Zero-Shot Document Retrieval

Shengyao Zhuang, Xueguang Ma, Bevan Koopman, Jimmy Lin, and Guido Zuccon.

Published in Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2024. Full paper

2023

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

Shengyao Zhuang.

Published in The University of Queensland, 2023. Thesis

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

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 Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR ’24), 2023. Full paper

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

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

Exploring the Representation Power of SPLADE Models

Joel Mackenziem, Shengyao Zhuang (equal contribution), Guido Zuccon

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

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

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

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

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

Reduce, Reuse, Recycle: Green Information Retrieval Research

Harry Scells, Shengyao Zhuang, Guido Zuccon ()

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

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

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

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

Reinforcement Online Learning to Rank with Unbiased Reward Shaping

Shengyao Zhuang, Zhihao Qiao, Guido Zuccon

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

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

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

Robustness of Neural Rankers to Typos: A Comparative Study

Shengyao Zhuang, Xinyu Mao, Guido Zuccon ()

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

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

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

Shengyao Zhuang

Publications

2024

2023

2022

2021

2020