|
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 |
|
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 |
|
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 |
|
2022 Other Outputs CharacterBERT and self-teaching for improving the robustness of dense retrievers on queries with typosZhuang, Shengyao and Zuccon, Guido (2022). CharacterBERT and self-teaching for improving the robustness of dense retrievers on queries with typos. |
|
2022 Other Outputs AgvaluateLi, Hang, Zuccon, Guido, Koopman, Bevan and Mourad, Ahmed (2022). Agvaluate. The University of Queensland. (Dataset) doi: 10.48610/0160dc7 |
|
2019 Other Outputs Taskiir_study_dataZuccon, Guido, Deacon, Anthony and Koopman, Bevan (2019). Taskiir_study_data. The University of Queensland. (Dataset) doi: 10.14264/uql.2019.279 |
