Overview
Background
My research focuses on developing and applying data science methods to address real-world challenges in the legal domain, particularly in legal information retrieval, legal reasoning, and legal decision-making. I am especially interested in graph neural networks (GNNs) and large language models (LLMs).
Availability
- Dr Yanran Tang is:
- Available for supervision
Qualifications
- Masters (Research) of Law, South China University of Technology
- Doctor of Philosophy of Computer Science, The University of Queensland
Works
Search Professor Yanran Tang’s works on UQ eSpace
2026
Other Outputs
Structure-aware legal case retrieval
Tang, Yanran (2026). Structure-aware legal case retrieval. PhD Thesis, School of Electrical Engineering and Computer Science, The University of Queensland. doi: 10.14264/9cd51d0
2026
Journal Article
Cassette: case-to-case structural distillation for efficient legal case retrieval
Tang, Yanran, Qiu, Ruihong, Yin, Hongzhi, Li, Xue and Huang, Zi (2026). Cassette: case-to-case structural distillation for efficient legal case retrieval. ACM Transactions on Information Systems, 44 (5) 3816732, 1-27. doi: 10.1145/3816732
2026
Journal Article
LEXA: legal case retrieval via graph contrastive learning with contextualised LLM embeddings
Tang, Yanran, Qiu, Ruihong, Liu, Yilun, Li, Xue and Huang, Zi (2026). LEXA: legal case retrieval via graph contrastive learning with contextualised LLM embeddings. World Wide Web, 29 (2) 20, 1-30. doi: 10.1007/s11280-026-01407-w
2026
Conference Publication
ReaKase-8B: legal case retrieval via knowledge and reasoning representations with LLMs
Tang, Yanran, Qiu, Ruihong, Li, Xue and Huang, Zi (2026). ReaKase-8B: legal case retrieval via knowledge and reasoning representations with LLMs. 36th Australasian Database Conference, ADC 2025, Sydney, NSW, Australia and Bali, Indonesia, 4-6 December 2025. Heidelberg, Germany: Springer. doi: 10.1007/978-981-95-6196-4_22
2025
Journal Article
PUMA: efficient continual graph learning for node classification with graph condensation
Liu, Yilun, Qiu, Ruihong, Tang, Yanran, Yin, Hongzhi and Huang, Zi (2025). PUMA: efficient continual graph learning for node classification with graph condensation. IEEE Transactions on Knowledge and Data Engineering, 37 (1), 449-461. doi: 10.1109/tkde.2024.3485691
2024
Conference Publication
CaseLink: inductive graph learning for legal case retrieval
Tang, Yanran, Qiu, Ruihong, Yin, Hongzhi, Li, Xue and Huang, Zi (2024). CaseLink: inductive graph learning for legal case retrieval. 47th International ACM SIGIR Conference on Research and Development in Information Retrieval, Washington, DC, United States, 14-18 July 2024. New York, NY, United States: ACM. doi: 10.1145/3626772.3657693
2024
Conference Publication
CaseGNN: graph neural networks for legal case retrieval with text-attributed graphs
Tang, Yanran, Qiu, Ruihong, Liu, Yilun, Li, Xue and Huang, Zi (2024). CaseGNN: graph neural networks for legal case retrieval with text-attributed graphs. 46th European Conference on Information Retrieval, ECIR 2024, Glasgow, United Kingdom, 24 - 28 March 2024. Cham, Switzerland: Springer Nature Switzerland. doi: 10.1007/978-3-031-56060-6_6
2023
Conference Publication
Prompt-based effective input reformulation for legal case retrieval
Tang, Yanran, Qiu, Ruihong and Li, Xue (2023). Prompt-based effective input reformulation for legal case retrieval. 34th Australasian Database Conference, Melbourne, VIC Australia, 1-3 November 2023. Cham, Switzerland: Springer Cham. doi: 10.1007/978-3-031-47843-7_7
Supervision
Availability
- Dr Yanran Tang is:
- Available for supervision
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Media
Enquiries
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