6 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…
Retrieval-Augmented Review Generation for Poisoning Recommender Systems
Shiyi Yang, Xinshu Li, Guanglin Zhou +4
Recent studies have shown that recommender systems (RSs) are highly vulnerable to data poisoning attacks, where malicious actors inject fake user profiles, including a group of wel…
Causality-aligned Prompt Learning via Diffusion-based Counterfactual Generation
Xinshu Li, Ruoyu Wang, Erdun Gao +2
Prompt learning has garnered attention for its efficiency over traditional model training and fine-tuning. However, existing methods, constrained by inadequate theoretical foundati…
Efficient and Generalizable Environmental Understanding for Visual Navigation
Ruoyu Wang, Xinshu Li, Chen Wang +1
Visual Navigation is a core task in Embodied AI, enabling agents to navigate complex environments toward given objectives. Across diverse settings within Navigation tasks, many nec…
DrunkAgent: Stealthy Memory Corruption in LLM-Powered Recommender Agents
Shiyi Yang, Zhibo Hu, Xinshu Li +5
Large language model (LLM)-powered agents are increasingly used in recommender systems (RSs) to achieve personalized behavior modeling, where the memory mechanism plays a pivotal r…
Self-Distilled Disentangled Learning for Counterfactual Prediction
Xinshu Li, Mingming Gong, Lina Yao
The advancements in disentangled representation learning significantly enhance the accuracy of counterfactual predictions by granting precise control over instrumental variables, c…