67 citations · 109 across the 14 of their papers we have counts for
15 papers
Inference-time Alignment in Continuous Space
Yige Yuan, Teng Xiao, Li Yunfan +5
Aligning large language models with human feedback at inference time has received increasing attention due to its flexibility. Existing methods rely on generating multiple response…
Incentivizing Strong Reasoning from Weak Supervision
Yige Yuan, Teng Xiao, Shuchang Tao +4
Large language models (LLMs) have demonstrated impressive performance on reasoning-intensive tasks, but enhancing their reasoning abilities typically relies on either reinforcement…
ToolCoder: A Systematic Code-Empowered Tool Learning Framework for Large Language Models
Hanxing Ding, Shuchang Tao, Liang Pang +5
Tool learning has emerged as a crucial capability for large language models (LLMs) to solve complex real-world tasks through interaction with external tools. Existing approaches fa…
On the Diminishing Returns of Complex Robust RAG Training in the Era of Powerful LLMs
Hanxing Ding, Shuchang Tao, Liang Pang +5
Retrieval-augmented generation (RAG) systems traditionally employ sophisticated training strategies to enhance robustness against retrieval noise. In this work, we investigate a cr…
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…
Robust Recommender System: A Survey and Future Directions
Kaike Zhang, Qi Cao, Fei Sun +4
With the rapid growth of information, recommender systems have become integral for providing personalized suggestions and overcoming information overload. However, their practical…
Popularity Debiasing from Exposure to Interaction in Collaborative Filtering
Yuanhao Liu, Qi Cao, Huawei Shen +3
Recommender systems often suffer from popularity bias, where popular items are overly recommended while sacrificing unpopular items. Existing researches generally focus on ensuring…
IDEA: Invariant Defense for Graph Adversarial Robustness
Shuchang Tao, Qi Cao, Huawei Shen +3
Despite the success of graph neural networks (GNNs), their vulnerability to adversarial attacks poses tremendous challenges for practical applications. Existing defense methods suf…
Graph Adversarial Immunization for Certifiable Robustness
Shuchang Tao, Huawei Shen, Qi Cao +3
Despite achieving great success, graph neural networks (GNNs) are vulnerable to adversarial attacks. Existing defenses focus on developing adversarial training or model modificatio…
Adversarial Camouflage for Node Injection Attack on Graphs
Shuchang Tao, Qi Cao, Huawei Shen +4
Node injection attacks on Graph Neural Networks (GNNs) have received increasing attention recently, due to their ability to degrade GNN performance with high attack success rates.…
Single Node Injection Attack against Graph Neural Networks
Shuchang Tao, Qi Cao, Huawei Shen +3
Node injection attack on Graph Neural Networks (GNNs) is an emerging and practical attack scenario that the attacker injects malicious nodes rather than modifying original nodes or…
Signed Bipartite Graph Neural Networks
Junjie Huang, Huawei Shen, Qi Cao +2
Signed networks are such social networks having both positive and negative links. A lot of theories and algorithms have been developed to model such networks (e.g., balance theory)…
INMO: A Model-Agnostic and Scalable Module for Inductive Collaborative Filtering
Yunfan Wu, Qi Cao, Huawei Shen +2
Collaborative filtering is one of the most common scenarios and popular research topics in recommender systems. Among existing methods, latent factor models, i.e., learning a speci…
Adversarial Immunization for Certifiable Robustness on Graphs
Shuchang Tao, Huawei Shen, Qi Cao +2
Despite achieving strong performance in semi-supervised node classification task, graph neural networks (GNNs) are vulnerable to adversarial attacks, similar to other deep learning…