Overview
Background
Hang Li is a Research Officer and graduating PhD candidate in IELab within the School of Information Technology and Electrical Engineering at the University of Queensland, Australia, where he works closely with Prof. Guido Zuccon, A/Prof. Bevan Koopman, and Dr. Ahmed Mourad. Prior to Ph.D, Hang received his Bachelor of Science degree in Computer Science at the University of Minnesota Twin-Cities in United States in 2016.
Hang works at the intersection of Information Retrieval (IR), Large Language Models (LLMs), Natural Language Processing (NLP), and Machine Learning (ML) applications, where he utilises different relevance feedbacks to empower the information retrieval system. His recent work seeks to address the gap between relevance feedback, deep language models, and information retrieval through different approaches that helps to improve the IR system effectiveness with minimal efficiency cost.
Hang publishes at premier academic venues in IR (e.g. SIGIR, ECIR, WSDM, WWW, TOIS, IJDL). His work is supported by Grains Research and Development Corporation, through the AgAsk project.
Availability
- Mr Hang Li is:
- Available for supervision
Fields of research
Qualifications
- Bachelor of Computer Science, University of Minnesota-Twin Cities
Works
Search Professor Hang Li’s works on UQ eSpace
2025
Other Outputs
Pseudo relevance feedback is enough to close the gap between small and large dense retrieval models
Li, Hang, Wang, Xiao, Koopman, Bevan and Zuccon, Guido (2025). Pseudo relevance feedback is enough to close the gap between small and large dense retrieval models. doi: 10.48550/arXiv.2503.14887
2025
Other Outputs
LLM-VPRF: Large language model based vector pseudo relevance feedback
Li, Hang, Zhuang, Shengyao, Koopman, Bevan and Zuccon, Guido (2025). LLM-VPRF: Large language model based vector pseudo relevance feedback. doi: 10.48550/arXiv.2504.01448
2024
Other Outputs
TPRF: A transformer-based pseudo-relevance feedback model for efficient and effective retrieval
Li, Hang, Yu, Chuting, Mourad, Ahmed, Koopman, Bevan and Zuccon, Guido (2024). TPRF: A transformer-based pseudo-relevance feedback model for efficient and effective retrieval. doi: 10.48550/arXiv.2401.13509
2024
Journal Article
AgAsk: an agent to help answer farmer’s questions from scientific documents
Koopman, Bevan, Mourad, Ahmed, Li, Hang, van der Vegt, Anton, Zhuang, Shengyao, Gibson, Simon, Dang, Yash, Lawrence, David and Zuccon, Guido (2024). AgAsk: an agent to help answer farmer’s questions from scientific documents. International Journal on Digital Libraries, 25 (4), 569-584. doi: 10.1007/s00799-023-00369-y
2023
Conference Publication
AgAsk: A conversational search agent for answering agricultural questions
Li, Hang, Koopman, Bevan, Mourad, Ahmed and Zuccon, Guido (2023). AgAsk: A conversational search agent for answering agricultural questions. 16th ACM International Conference on Web Search and Data Mining, WSDM 2023, Singapore, 27 - 3 March 2023. New York, NY United States: ACM. doi: 10.1145/3539597.3573034
2023
Conference Publication
MeSH suggester: a library and system for MeSH term suggestion for systematic review Boolean query construction
Wang, Shuai, Li, Hang and Zuccon, Guido (2023). MeSH suggester: a library and system for MeSH term suggestion for systematic review Boolean query construction. Sixteenth ACM International Conference on Web Search and Data Mining, Singapore, Singapore, 27 February - 3 March 2023. New York, NY, United States: ACM. doi: 10.1145/3539597.3573025
2023
Journal Article
Pseudo relevance feedback with deep language models and dense retrievers: successes and pitfalls
Li, Hang, Mourad, Ahmed, Zhuang, Shengyao, Koopman, Bevan and Zuccon, Guido (2023). Pseudo relevance feedback with deep language models and dense retrievers: successes and pitfalls. ACM Transactions on Information Systems, 41 (3) 62, 1-40. doi: 10.1145/3570724
2022
Conference Publication
Pseudo-relevance feedback with dense retrievers in Pyserini
Li, Hang, Zhuang, Shengyao, Ma, Xueguang, Lin, Jimmy and Zuccon, Guido (2022). Pseudo-relevance feedback with dense retrievers in Pyserini. ADCS '22: Australasian Document Computing Symposium, Adelaide, SA, Australia, 15-16 December 2022. New York, United States: Association for Computing Machinery. doi: 10.1145/3572960.3572982
2022
Conference Publication
Implicit feedback for dense passage retrieval: a counterfactual approach
Zhuang, Shengyao, Li, Hang and Zuccon, Guido (2022). Implicit feedback for dense passage retrieval: a counterfactual approach. 45th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), Madrid, Spain, 11 - 15 July 2022. New York, NY, United States: Association for Computing Machinery. doi: 10.1145/3477495.3531994
2022
Conference Publication
To Interpolate or not to Interpolate: PRF, dense and sparse retrievers
Li, Hang, Wang, Shuai, Zhuang, Shengyao, Mourad, Ahmed, Ma, Xueguang, Lin, Jimmy and Zuccon, Guido (2022). To Interpolate or not to Interpolate: PRF, dense and sparse retrievers. SIGIR '22: The 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, Madrid, Spain, 11-15 July 2022. New York, United States: Association for Computing Machinery. doi: 10.1145/3477495.3531884
2022
Conference Publication
How does feedback signal quality impact effectiveness of pseudo relevance feedback for passage retrieval
Li, Hang, Mourad, Ahmed, Koopman, Bevan and Zuccon, Guido (2022). How does feedback signal quality impact effectiveness of pseudo relevance feedback for passage retrieval. 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, Madrid, Spain, 11 - 15 July 2022. New York, NY, United States: Association for Computing Machinery. doi: 10.1145/3477495.3531822
2022
Other Outputs
Agvaluate
Li, Hang, Zuccon, Guido, Koopman, Bevan and Mourad, Ahmed (2022). Agvaluate. The University of Queensland. (Dataset) doi: 10.48610/0160dc7
2022
Conference Publication
Improving query representations for dense retrieval with pseudo relevance feedback: a reproducibility study
Li, Hang, Zhuang, Shengyao, Mourad, Ahmed, Ma, Xueguang, Lin, Jimmy and Zuccon, Guido (2022). Improving query representations for dense retrieval with pseudo relevance feedback: a reproducibility study. 44th European Conference on IR Research, ECIR 2022, Stavanger, Norway, 10-14 April 2022. Cham, Switzerland: Springer International Publishing. doi: 10.1007/978-3-030-99736-6_40
2021
Conference Publication
MeSH Term Suggestion for Systematic Review Literature Search
Wang, Shuai, Li, Hang, Scells, Harrisen, Locke, Daniel and Zuccon, Guido (2021). MeSH Term Suggestion for Systematic Review Literature Search. Australasian Document Computing Symposium, Melbourne, VIC, Australia, 9 December 2021. New York, NY, United States: Association for Computing Machinery. doi: 10.1145/3503516.3503530
2021
Conference Publication
Deep query likelihood model for information retrieval
Zhuang, Shengyao, Li, Hang and Zuccon, Guido (2021). Deep query likelihood model for information retrieval. The 43rd European Conference On Information Retrieval (ECIR), Lucca, Italy - online event, March 28–April 1, 2021. Cham, Switzerland: Elsevier. doi: 10.1007/978-3-030-72240-1_49
2021
Journal Article
Design and research of intelligent question-answering(Q&A) system based on high school course knowledge graph
Yang, Zhijun, Wang, Yang, Gan, Jianhou, Li, Hang and Lei, Ning (2021). Design and research of intelligent question-answering(Q&A) system based on high school course knowledge graph. Mobile Networks and Applications, 26 (5), 1884-1890. doi: 10.1007/s11036-020-01726-w
2020
Conference Publication
Systematic review automation tools for end-to-end query formulation
Li, Hang, Scells, Harrisen and Zuccon, Guido (2020). Systematic review automation tools for end-to-end query formulation. SIGIR '20: The 43rd International ACM SIGIR conference on research and development in Information Retrieval, Virtual, July 2020. New York, NY USA: Association for Computing Machinery. doi: 10.1145/3397271.3401402
2020
Conference Publication
IELAB for TREC Conversational Assistance Track (CAsT) 2020
Sebastian, Cross, Li, Hang, Zhuang, Arvin, Ahmed, Mourad, Bevan, Koopman and Guido, Zuccon (2020). IELAB for TREC Conversational Assistance Track (CAsT) 2020. 29th Text REtrieval Conference, TREC 2020, Online, 16-20 November 2020. Gaithersburg, MD United States: National Institute of Standards and Technology (NIST).
Supervision
Availability
- Mr Hang Li is:
- Available for supervision
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Media
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