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2026 Other Outputs Advanced query representation and feedback methods for neural information retrievalLi, Hang (2026). Advanced query representation and feedback methods for neural information retrieval. PhD Thesis, School of Electrical Engineering and Computer Science, The University of Queensland. doi: 10.14264/a3cd0b3 |
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2025 Other Outputs Pseudo relevance feedback is enough to close the gap between small and large dense retrieval modelsLi, 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 |
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2025 Other Outputs LLM-VPRF: Large language model based vector pseudo relevance feedbackLi, 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 |
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2024 Other Outputs TPRF: A transformer-based pseudo-relevance feedback model for efficient and effective retrievalLi, 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 |
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2022 Other Outputs AgvaluateLi, Hang, Zuccon, Guido, Koopman, Bevan and Mourad, Ahmed (2022). Agvaluate. The University of Queensland. (Dataset) doi: 10.48610/0160dc7 |
