Hang Li is a Postdoctoral Research Fellow at ielab in the School of Electrical Engineering and Computer Science at the University of Queensland, Australia, where he also completed his PhD working closely with Prof. Guido Zuccon, Dr. Bevan Koopman, and Dr. Ahmed Mourad. Prior to his PhD, Hang received his Bachelor of Science degree in Computer Science from the University of Minnesota Twin Cities in the United States in 2016.
Hang works at the intersection of Information Retrieval, Natural Language Processing (NLP), and Large Language Models (LLMs), where he utilises different forms of relevance feedback to empower search systems. His work seeks to address the gap between relevance feedback, deep language models, and conversational search through approaches that improve search effectiveness with minimal efficiency cost.
Hang publishes at premier academic venues in IR (e.g., SIGIR, ECIR). His work has been supported by the Grains Research and Development Corporation through the AgAsk project.
Projects
Publications (24)
2026
4 publications- When LLM Judges Inflate Scores: Exploring Overrating in Relevance Assessment
arXiv preprint arXiv:2602.17170 · 2026
- Advanced Query Representation and Feedback Methods for Neural Information Retrieval
The University of Queensland · 2026
- Rapid, Agile Development and Evaluation of Retrieval Augmented Generation Systems Without Labels
Lecture notes in computer science · 2026
- Whole-Pool Setwise Reranking with Long-Context Language Models
arXiv preprint arXiv:2606.01782 · 2026
2025
3 publications- LLM-VPRF: Large Language Model Based Vector Pseudo Relevance Feedback
arXiv preprint arXiv:2504.01448 · 2025
- Pseudo-Relevance Feedback Can Improve Zero-Shot LLM-Based Dense Retrieval
arXiv e-prints, arXiv: 2503.14887 · 2025
- Pseudo Relevance Feedback is Enough to Close the Gap Between Small and Large Dense Retrieval Models
ArXiv.org · 2025
2024
1 publication- TPRF: A Transformer-based Pseudo-Relevance Feedback Model for Efficient and Effective Retrieval
arXiv (Cornell University) · 2024
2023
4 publications- Pseudo Relevance Feedback with Deep Language Models and Dense Retrievers: Successes and Pitfalls
ACM Transactions on Information Systems · 2023
- AgAsk: an agent to help answer farmer’s questions from scientific documents
International Journal on Digital Libraries · 2023
- MeSH Suggester: A Library and System for MeSH Term Suggestion for Systematic Review Boolean Query Construction
2023
- AgAsk: A Conversational Search Agent for Answering Agricultural Questions
2023
2022
6 publications- To interpolate or not to interpolate: Prf, dense and sparse retrievers
Proceedings of the 45th international ACM SIGIR conference on research and · 2022
- Implicit feedback for dense passage retrieval: A counterfactual approach
Proceedings of the 45th International ACM SIGIR Conference on Research and · 2022
- Improving Query Representations for Dense Retrieval with Pseudo Relevance Feedback: A Reproducibility Study
Lecture notes in computer science · 2022
- How Does Feedback Signal Quality Impact Effectiveness of Pseudo Relevance Feedback for Passage Retrieval
Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval · 2022
- Pseudo-Relevance Feedback with Dense Retrievers in Pyserini
2022
- Agvaluate
The University of Queensland · 2022
2021
3 publications- Design and Research of Intelligent Question-Answering(Q&A) System Based on High School Course Knowledge Graph
Mobile Networks and Applications · 2021
- Deep Query Likelihood Model for Information Retrieval
Lecture notes in computer science · 2021
- Mesh term suggestion for systematic review literature search
Proceedings of the 25th Australasian Document Computing Symposium, 1-8 · 2021
2020
3 publications- Systematic Review Automation Tools for End-to-End Query Formulation
2020
- IELAB at TREC Deep Learning Track 2021
2020
- IELAB for TREC Conversational Assistance Track (CAsT) 2020.
Text REtrieval Conference · 2020