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

2026

LACONIC: Dense-Level Effectiveness for Scalable Sparse Retrieval via a Two-Phase Training Curriculum

Published in Preprint, 2026. Full paper

2025

Tevatron 2.0: Unified document retrieval toolkit across scale, language, and modality

Published in Preprint, 2025. Full paper

Set-encoder: Permutation-invariant inter-passage attention for listwise passage re-ranking with cross-encoders

Published in Preprint, 2025. Full paper

Report from the 4th Strategic Workshop on Information Retrieval in Lorne (SWIRL 2025)

Published in Preprint, 2025. Full paper

SIGIR-AP 2025 Tutorial on Retrieval and Ranking with LLMs (R2LLMs)

Published in Preprint, 2025. Full paper

Rethinking On-policy Optimization for Query Augmentation

Published in Preprint, 2025. Full paper

Resllm: Large language models are strong resource selectors for federated search

Published in Preprint, 2025. Full paper

Rank-r1: Enhancing reasoning in llm-based document rerankers via reinforcement learning

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

Published in arXiv preprint arXiv:2503.06034, 2025. Full paper

Rank-distillm: Closing the effectiveness gap between cross-encoders and llms for passage re-ranking

Ferdinand Schlatt, Maik Fröbe, Harrisen Scells, Shengyao Zhuang, Bevan Koopman, Guido Zuccon, Benno Stein, Martin Potthast, Matthias Hagen

Published in Preprint, 2025. Full paper

R2LLMs: Retrieval and Ranking with LLMs

Published in Preprint, 2025. Full paper

MAGMaR Shared Task System Description: Video Retrieval with OmniEmbed

Published in Preprint, 2025. Full paper

Leveraging Reference Documents for Zero-Shot Ranking via Large Language Models

Published in Preprint, 2025. Full paper

LLM-VPRF: Large Language Model Based Vector Pseudo Relevance Feedback

Published in Preprint, 2025. Full paper

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

Shuoqi Sun, Shengyao Zhuang, Shuai Wang, Guido Zuccon

Published in Preprint, 2025. Full paper

The impact of auxiliary patient data on automated chest x-ray report generation and how to incorporate it

Published in Preprint, 2025. Full paper

Document screenshot retrievers are vulnerable to pixel poisoning attacks

Published in Preprint, 2025. Full paper

Distillation versus contrastive learning: How to train your rerankers

Published in Preprint, 2025. Full paper

2d matryoshka training for information retrieval

Published in Preprint, 2025. Full paper

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

Published in Preprint, 2025. Full paper

Browsecomp-plus: A more fair and transparent evaluation benchmark of deep-research agent

Zijian Chen*, Xueguang Ma*, Shengyao Zhuang*, Ping Nie, Kai Zou, Andrew Liu, Joshua Green, Kshama Patel, Ruoxi Meng, Mingyi Su, Sahel Sharifymoghaddam, Yanxi Li, Haoran Hong, Xinyu Shi, Xuye Liu, Nandan Thakur, Crystina Zhang, Luyu Gao, Wenhu Chen, Jimmy Lin

Published in arXiv preprint arXiv:2508.06600, 2025. Full paper

Visa: Retrieval augmented generation with visual source attribution

Xueguang Ma, Shengyao Zhuang, Bevan Koopman, Guido Zuccon, Wenhu Chen, Jimmy Lin

Published in Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2025. Full paper

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

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

Shuai Wang, Harrisen Scells, Shengyao Zhuang, Martin Potthast, Bevan Koopman, Guido Zuccon

Published in Preprint, 2024. Full paper

Team ielab at trec clinical trial track 2023: Enhancing clinical trial retrieval with neural rankers and large language models

Published in Preprint, 2024. Full paper

Starbucks-v2: Improved Training for 2D Matryoshka Embeddings

Published in Preprint, 2024. Full paper

Revisiting Document Expansion and Filtering for Effective First-Stage Retrieval

Published in Preprint, 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

Large language models based stemming for information retrieval: Promises, pitfalls and failures

Published in Preprint, 2024. Full paper

Embark on DenseQuest: A System for Selecting the Best Dense Retriever for a Custom Collection

Published in Preprint, 2024. Full paper

Does Vec2Text Pose a New Corpus Poisoning Threat?

Published in Preprint, 2024. Full paper

Dense Retrieval with Continuous Explicit Feedback for Systematic Review Screening Prioritisation

Published in Preprint, 2024. Full paper

AgAsk: an agent to help answer farmer’s questions from scientific documents

Published in Preprint, 2024. Full paper

FeB4RAG: Evaluating Federated Search in the Context of Retrieval Augmented Generation

Shuai Wang, Ekaterina Khramtsova, Shengyao Zhuang, Guido Zuccon

Published in Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval, 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

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

IELAB at TREC Deep Learning Track 2021

Published in Preprint, 2021. Full paper

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

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

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

Effective and Privacy-preserving Federated Online Learning to Rank

Shuyi Wang, Bing Liu, 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

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

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

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

2020

IELAB for TREC Conversational Assistance Track (CAsT) 2020

Published in Preprint, 2020. Full paper

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