School of Electrical Engineering and Computer Science

Mr Hang Li

Research Officer
School of Electrical Engineering and Computer Science
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Researcher biography

Hang is a Postdoctoral Research Officer in IELab within the School of Electrical Engineering and Computer Science 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, he received his Bachelor of Science degree in Computer Science at the University of Minnesota Twin-Cities, United States, 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 works seek 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 was supported by Grains Research and Development Corporation, through the AgAsk project during Ph.D.

Publications

Journal Articles (3)
Conference Papers (11)
Data Collection (1)
Preprints (3)

Journal Articles

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
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
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

Conference Papers

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
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
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
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
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
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
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
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
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
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
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).

Data Collection

Li, Hang, Zuccon, Guido, Koopman, Bevan and Mourad, Ahmed (2022). Agvaluate. The University of Queensland. (Dataset) doi: 10.48610/0160dc7

Preprints

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
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
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