8 citations · 11 across the 12 of their papers we have counts for
15 papers
Decouple to Forget: Semantic Mediation for Sensitive Unlearning in Large Language Models
Jingyue Cong, Andy Song, Yang Yu +7
Machine unlearning for large language models seeks to remove specified knowledge while preserving general capability, yet current methods often treat an entire response as the unit…
The Commit-Abstain Circuit: Why Language Models Hallucinate Instead of Abstaining
Vy Nguyen, Ziqi Xu, Jeffrey Chan +5
Language models (LMs) often hallucinate by committing to confident answers rather than abstaining, even when they do not have enough information to answer reliably. A large body of…
Twin Worlds: Equivariance-Based Abstention for Evidence-Grounded Reasoning
Vy Nguyen, Ziqi Xu, Jeffrey Chan +5
Knowledge-intensive reasoning requires Large Language Models (LLMs) to ground answers in provided evidence. When evidence is insufficient, it is desirable that models abstain rathe…
The "Curse of Knowledge" in LLM Query Simulation: Concept Provenance for Tracing Answer-Side Intrusion
Chenglong Ma, Xinye Wanyan, Danula Hettiachchi +2
LLM-generated search queries are widely used to augment IR evaluation, yet they may contain concepts that presuppose answer-side document knowledge, violating the information-acces…
Task-Aware Automated User Profile Generation for Recommendation Simulation Using Large Language Models
Xinye Wanyan, Chenglong Ma, Danula Hettiachchi +2
Large Language Model (LLM)-based agent simulation has emerged as a promising approach to meet the increasing demand for real-time and rigorous evaluation in modern recommender syst…
PrimeKG-CL: A Continual Graph Learning Benchmark on Evolving Biomedical Knowledge Graphs
Yousef A. Radwan, Yao Li, Qing Qing +5
Biomedical knowledge graphs underwrite drug repurposing and clinical decision support, yet the upstream ontologies they depend on update on independent cycles that add millions of…
CMKL: Modality-Aware Continual Learning for Evolving Biomedical Knowledge Graphs
Yousef A. Radwan, Yao Li, Qing Qing +5
Biomedical knowledge graphs are increasingly large, dynamic, and multimodal, driven by rapid advances in biotechnology such as high-throughput sequencing. Machine learning models c…
UFO: A Unified Flow-Oriented Framework for Robust Continual Graph Learning
Danhui Zhang, Zhe Wang, Qing Qing +6
Graph learning research has increasingly shifted toward continual graph learning (CGL), which better reflects real-world scenarios where graphs evolve over time. However, existing…
Stable Multimodal Graph Unlearning via Feature-Dimension Aware Quantile Selection
Jingjing Zhou, Yongshuai Yang, Qing Qing +5
Graph unlearning remains a critical technique for supporting privacy-preserving and sustainable multimodal graph learning. However, we observe that existing unlearning strategies t…
MCLMR: A Model-Agnostic Causal Learning Framework for Multi-Behavior Recommendation
Ranxu Zhang, Junjie Meng, Ying Sun +5
Multi-Behavior Recommendation (MBR) leverages multiple user interaction types (e.g., views, clicks, purchases) to enrich preference modeling and alleviate data sparsity issues in t…
Hallucinate Less by Thinking More: Aspect-Based Causal Abstention for Large Language Models
Vy Nguyen, Ziqi Xu, Jeffrey Chan +3
Large Language Models (LLMs) often produce fluent but factually incorrect responses, a phenomenon known as hallucination. Abstention, where the model chooses not to answer and inst…
Synthetic Forgetting without Access: A Few-shot Zero-glance Framework for Machine Unlearning
Qipeng Song, Nan Yang, Ziqi Xu +3
Machine unlearning aims to eliminate the influence of specific data from trained models to ensure privacy compliance. However, most existing methods assume full access to the origi…
Temporal-Aware User Behaviour Simulation with Large Language Models for Recommender Systems
Xinye Wanyan, Danula Hettiachchi, Chenglong Ma +2
Large Language Models (LLMs) demonstrate human-like capabilities in language understanding, reasoning, and generation, driving interest in using LLM-based agents to simulate human…
FairDRL-ST: Disentangled Representation Learning for Fair Spatio-Temporal Mobility Prediction
Sichen Zhao, Wei Shao, Jeffrey Chan +2
As deep spatio-temporal neural networks are increasingly utilised in urban computing contexts, the deployment of such methods can have a direct impact on users of critical urban in…
PUB: An LLM-Enhanced Personality-Driven User Behaviour Simulator for Recommender System Evaluation
Chenglong Ma, Ziqi Xu, Yongli Ren +2
Traditional offline evaluation methods for recommender systems struggle to capture the complexity of modern platforms due to sparse behavioural signals, noisy data, and limited mod…