78 citations · 277 across the 32 of their papers we have counts for
5 papers · 1 filter
Understanding and Improving Adversarial Collaborative Filtering for Robust Recommendation
Kaike Zhang, Qi Cao, Yunfan Wu +3
Adversarial Collaborative Filtering (ACF), which typically applies adversarial perturbations at user and item embeddings through adversarial training, is widely recognized as an ef…
Improving the Shortest Plank: Vulnerability-Aware Adversarial Training for Robust Recommender System
Kaike Zhang, Qi Cao, Yunfan Wu +3
Recommender systems play a pivotal role in mitigating information overload in various fields. Nonetheless, the inherent openness of these systems introduces vulnerabilities, allowi…
Accelerating the Surrogate Retraining for Poisoning Attacks against Recommender Systems
Yunfan Wu, Qi Cao, Shuchang Tao +3
Recent studies have demonstrated the vulnerability of recommender systems to data poisoning attacks, where adversaries inject carefully crafted fake user interactions into the trai…
When to Trust LLMs: Aligning Confidence with Response Quality
Shuchang Tao, Liuyi Yao, Hanxing Ding +6
Despite the success of large language models (LLMs) in natural language generation, much evidence shows that LLMs may produce incorrect or nonsensical text. This limitation highlig…
Blinded by Generated Contexts: How Language Models Merge Generated and Retrieved Contexts When Knowledge Conflicts?
Hexiang Tan, Fei Sun, Wanli Yang +3
While auxiliary information has become a key to enhancing Large Language Models (LLMs), relatively little is known about how LLMs merge these contexts, specifically contexts genera…