Shengyao Zhuang

Orcid: 0000-0002-6711-0955

Affiliations:
  • University of Queensland, Brisbane, Queensland, Australia


According to our database1, Shengyao Zhuang authored at least 35 papers between 2020 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

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Bibliography

2024
Understanding and Mitigating the Threat of Vec2Text to Dense Retrieval Systems.
CoRR, 2024

FeB4RAG: Evaluating Federated Search in the Context of Retrieval Augmented Generation.
CoRR, 2024

Large Language Models for Stemming: Promises, Pitfalls and Failures.
CoRR, 2024

Leveraging LLMs for Unsupervised Dense Retriever Ranking.
CoRR, 2024

ReSLLM: Large Language Models are Strong Resource Selectors for Federated Search.
CoRR, 2024

Team IELAB at TREC Clinical Trial Track 2023: Enhancing Clinical Trial Retrieval with Neural Rankers and Large Language Models.
CoRR, 2024

Zero-Shot Generative Large Language Models for Systematic Review Screening Automation.
Proceedings of the Advances in Information Retrieval, 2024

2023
Pseudo Relevance Feedback with Deep Language Models and Dense Retrievers: Successes and Pitfalls.
ACM Trans. Inf. Syst., 2023

A Setwise Approach for Effective and Highly Efficient Zero-shot Ranking with Large Language Models.
CoRR, 2023

Augmenting Passage Representations with Query Generation for Enhanced Cross-Lingual Dense Retrieval.
Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2023

Typos-aware Bottlenecked Pre-Training for Robust Dense Retrieval.
Proceedings of the Annual International ACM SIGIR Conference on Research and Development in Information Retrieval in the Asia Pacific Region, 2023

Selecting which Dense Retriever to use for Zero-Shot Search.
Proceedings of the Annual International ACM SIGIR Conference on Research and Development in Information Retrieval in the Asia Pacific Region, 2023

Beyond CO2 Emissions: The Overlooked Impact of Water Consumption of Information Retrieval Models.
Proceedings of the 2023 ACM SIGIR International Conference on Theory of Information Retrieval, 2023

Exploring the Representation Power of SPLADE Models.
Proceedings of the 2023 ACM SIGIR International Conference on Theory of Information Retrieval, 2023

Open-source Large Language Models are Strong Zero-shot Query Likelihood Models for Document Ranking.
Proceedings of the Findings of the Association for Computational Linguistics: EMNLP 2023, 2023

2022
Reinforcement online learning to rank with unbiased reward shaping.
Inf. Retr. J., 2022

AgAsk: An Agent to Help Answer Farmer's Questions From Scientific Documents.
CoRR, 2022

Bridging the Gap Between Indexing and Retrieval for Differentiable Search Index with Query Generation.
CoRR, 2022

Asyncval: A Toolkit for Asynchronously Validating Dense Retriever Checkpoints During Training.
Proceedings of the SIGIR '22: The 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, Madrid, Spain, July 11, 2022

CharacterBERT and Self-Teaching for Improving the Robustness of Dense Retrievers on Queries with Typos.
Proceedings of the SIGIR '22: The 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, Madrid, Spain, July 11, 2022

Implicit Feedback for Dense Passage Retrieval: A Counterfactual Approach.
Proceedings of the SIGIR '22: The 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, Madrid, Spain, July 11, 2022

Reduce, Reuse, Recycle: Green Information Retrieval Research.
Proceedings of the SIGIR '22: The 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, Madrid, Spain, July 11, 2022

To Interpolate or not to Interpolate: PRF, Dense and Sparse Retrievers.
Proceedings of the SIGIR '22: The 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, Madrid, Spain, July 11, 2022

Improving Query Representations for Dense Retrieval with Pseudo Relevance Feedback: A Reproducibility Study.
Proceedings of the Advances in Information Retrieval, 2022

Robustness of Neural Rankers to Typos: A Comparative Study.
Proceedings of the 26th Australasian Document Computing Symposium, 2022

Pseudo-Relevance Feedback with Dense Retrievers in Pyserini.
Proceedings of the 26th Australasian Document Computing Symposium, 2022

2021
Fast Passage Re-ranking with Contextualized Exact Term Matching and Efficient Passage Expansion.
CoRR, 2021

TILDE: Term Independent Likelihood moDEl for Passage Re-ranking.
Proceedings of the SIGIR '21: The 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2021

How do Online Learning to Rank Methods Adapt to Changes of Intent?
Proceedings of the SIGIR '21: The 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2021

BERT-based Dense Retrievers Require Interpolation with BM25 for Effective Passage Retrieval.
Proceedings of the ICTIR '21: The 2021 ACM SIGIR International Conference on the Theory of Information Retrieval, 2021

Effective and Privacy-preserving Federated Online Learning to Rank.
Proceedings of the ICTIR '21: The 2021 ACM SIGIR International Conference on the Theory of Information Retrieval, 2021

Dealing with Typos for BERT-based Passage Retrieval and Ranking.
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, 2021

Deep Query Likelihood Model for Information Retrieval.
Proceedings of the Advances in Information Retrieval, 2021

Federated Online Learning to Rank with Evolution Strategies: A Reproducibility Study.
Proceedings of the Advances in Information Retrieval, 2021

2020
Counterfactual Online Learning to Rank.
Proceedings of the Advances in Information Retrieval, 2020


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