Shengyao Zhuang
Orcid: 0000-0002-6711-0955Affiliations:
- University of Queensland, Brisbane, Queensland, Australia
According to our database1,
Shengyao Zhuang
authored at least 48 papers
between 2020 and 2024.
Collaborative distances:
Collaborative distances:
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on ielab.io
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Bibliography
2024
Int. J. Digit. Libr., December, 2024
The Impact of Auxiliary Patient Data on Automated Chest X-Ray Report Generation and How to Incorporate It.
CoRR, 2024
A Systematic Investigation of Distilling Large Language Models into Cross-Encoders for Passage Re-ranking.
CoRR, 2024
Set-Encoder: Permutation-Invariant Inter-Passage Attention for Listwise Passage Re-Ranking with Cross-Encoders.
CoRR, 2024
CoRR, 2024
A Setwise Approach for Effective and Highly Efficient Zero-shot Ranking with Large Language Models.
Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2024
Large Language Models Based Stemming for Information Retrieval: Promises, Pitfalls and Failures.
Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2024
FeB4RAG: Evaluating Federated Search in the Context of Retrieval Augmented Generation.
Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2024
Dense Retrieval with Continuous Explicit Feedback for Systematic Review Screening Prioritisation.
Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2024
Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2024
Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2024
Embark on DenseQuest: A System for Selecting the Best Dense Retriever for a Custom Collection.
Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2024
Proceedings of the 2024 Annual International ACM SIGIR Conference on Research and Development in Information Retrieval in the Asia Pacific Region, 2024
PromptReps: Prompting Large Language Models to Generate Dense and Sparse Representations for Zero-Shot Document Retrieval.
Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, 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
Team IELAB at TREC Clinical Trial Track 2023: Enhancing Clinical Trial Retrieval with Neural Rankers and Large Language Models.
Proceedings of the Thirty-Second Text REtrieval Conference Proceedings (TREC 2023), 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
Proceedings of the Annual International ACM SIGIR Conference on Research and Development in Information Retrieval in the Asia Pacific Region, 2023
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
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
Inf. Retr. J., 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
Proceedings of the SIGIR '22: The 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, Madrid, Spain, July 11, 2022
Proceedings of the SIGIR '22: The 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, Madrid, Spain, July 11, 2022
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
Proceedings of the 26th Australasian Document Computing Symposium, 2022
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
Proceedings of the SIGIR '21: The 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2021
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
Proceedings of the ICTIR '21: The 2021 ACM SIGIR International Conference on the Theory of Information Retrieval, 2021
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, 2021
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
Proceedings of the Advances in Information Retrieval, 2020