1 citations · 1 across the 12 of their papers we have counts for
13 papers
Cassette: Case-to-Case Structural Distillation for Efficient Legal Case Retrieval
Yanran Tang, Ruihong Qiu, Hongzhi Yin +2
Legal case retrieval (LCR) is an essential tool for not only assisting legal practitioners to efficiently retrieve precedents but also enabling ordinary individuals to find valuabl…
Call Neighbours Yourself: Graph Walks with Destination-Conditioned On-Policy Self-Distillation
Yilun Liu, Boyu Luo, Yanran Tang +2
Reasoning over text-attributed graphs (TAGs) requires large language models (LLMs) to combine a node's text with evidence distributed across its neighbourhood. Existing methods fix…
ReaKase-8B: Legal Case Retrieval via Knowledge and Reasoning Representations with LLMs
Yanran Tang, Ruihong Qiu, Xue Li +1
Legal case retrieval (LCR) is a cornerstone of real-world legal decision making, as it enables practitioners to identify precedents for a given query case. Existing approaches main…
Text Meets Topology: Rethinking Out-of-distribution Detection in Text-Rich Networks
Danny Wang, Ruihong Qiu, Guangdong Bai +1
Out-of-distribution (OOD) detection remains challenging in text-rich networks, where textual features intertwine with topological structures. Existing methods primarily address lab…
ALSA: Anchors in Logit Space for Out-of-Distribution Accuracy Estimation
Chenzhi Liu, Mahsa Baktashmotlagh, Yanran Tang +2
Estimating model accuracy on unseen, unlabeled datasets is crucial for real-world machine learning applications, especially under distribution shifts that can degrade performance.…
UQLegalAI@COLIEE2025: Advancing Legal Case Retrieval with Large Language Models and Graph Neural Networks
Yanran Tang, Ruihong Qiu, Zi Huang
Legal case retrieval plays a pivotal role in the legal domain by facilitating the efficient identification of relevant cases, supporting legal professionals and researchers to prop…
GOLD: Graph Out-of-Distribution Detection via Implicit Adversarial Latent Generation
Danny Wang, Ruihong Qiu, Guangdong Bai +1
Despite graph neural networks' (GNNs) great success in modelling graph-structured data, out-of-distribution (OOD) test instances still pose a great challenge for current GNNs. One…
GCondenser: Benchmarking Graph Condensation
Yilun Liu, Ruihong Qiu, Zi Huang
Large-scale graphs are valuable for graph representation learning, yet the abundant data in these graphs hinders the efficiency of the training process. Graph condensation (GC) all…
LEXA: Legal Case Retrieval via Graph Contrastive Learning with Contextualised LLM Embeddings
Yanran Tang, Ruihong Qiu, Yilun Liu +2
Legal case retrieval (LCR) is a specialised information retrieval task aimed at identifying relevant cases given a query case. LCR holds pivotal significance in facilitating legal…
CaseLink: Inductive Graph Learning for Legal Case Retrieval
Yanran Tang, Ruihong Qiu, Hongzhi Yin +2
In case law, the precedents are the relevant cases that are used to support the decisions made by the judges and the opinions of lawyers towards a given case. This relevance is ref…
CaseGNN: Graph Neural Networks for Legal Case Retrieval with Text-Attributed Graphs
Yanran Tang, Ruihong Qiu, Yilun Liu +2
Legal case retrieval is an information retrieval task in the legal domain, which aims to retrieve relevant cases with a given query case. Recent research of legal case retrieval ma…
PUMA: Efficient Continual Graph Learning for Node Classification with Graph Condensation
Yilun Liu, Ruihong Qiu, Yanran Tang +2
When handling streaming graphs, existing graph representation learning models encounter a catastrophic forgetting problem, where previously learned knowledge of these models is eas…
Prompt-based Effective Input Reformulation for Legal Case Retrieval
Yanran Tang, Ruihong Qiu, Xue Li
Legal case retrieval plays an important role for legal practitioners to effectively retrieve relevant cases given a query case. Most existing neural legal case retrieval models dir…