Search Results for author: Zeyu Qin

Found 8 papers, 6 papers with code

Step-On-Feet Tuning: Scaling Self-Alignment of LLMs via Bootstrapping

no code implementations12 Feb 2024 Haoyu Wang, Guozheng Ma, Ziqiao Meng, Zeyu Qin, Li Shen, Zhong Zhang, Bingzhe Wu, Liu Liu, Yatao Bian, Tingyang Xu, Xueqian Wang, Peilin Zhao

To further exploit the capabilities of bootstrapping, we investigate and adjust the training order of data, which yields improved performance of the model.

In-Context Learning

Beyond Factuality: A Comprehensive Evaluation of Large Language Models as Knowledge Generators

1 code implementation11 Oct 2023 Liang Chen, Yang Deng, Yatao Bian, Zeyu Qin, Bingzhe Wu, Tat-Seng Chua, Kam-Fai Wong

Large language models (LLMs) outperform information retrieval techniques for downstream knowledge-intensive tasks when being prompted to generate world knowledge.

Information Retrieval Informativeness +4

Towards Stable Backdoor Purification through Feature Shift Tuning

1 code implementation NeurIPS 2023 Rui Min, Zeyu Qin, Li Shen, Minhao Cheng

Our analysis shows that with the low poisoning rate, the entanglement between backdoor and clean features undermines the effect of tuning-based defenses.

Revisiting Personalized Federated Learning: Robustness Against Backdoor Attacks

1 code implementation3 Feb 2023 Zeyu Qin, Liuyi Yao, Daoyuan Chen, Yaliang Li, Bolin Ding, Minhao Cheng

We conduct the first study of backdoor attacks in the pFL framework, testing 4 widely used backdoor attacks against 6 pFL methods on benchmark datasets FEMNIST and CIFAR-10, a total of 600 experiments.

Backdoor Attack Personalized Federated Learning

Boosting the Transferability of Adversarial Attacks with Reverse Adversarial Perturbation

3 code implementations12 Oct 2022 Zeyu Qin, Yanbo Fan, Yi Liu, Li Shen, Yong Zhang, Jue Wang, Baoyuan Wu

Furthermore, RAP can be naturally combined with many existing black-box attack techniques, to further boost the transferability.

Adversarial Attack

Adaptive Smoothness-weighted Adversarial Training for Multiple Perturbations with Its Stability Analysis

1 code implementation2 Oct 2022 Jiancong Xiao, Zeyu Qin, Yanbo Fan, Baoyuan Wu, Jue Wang, Zhi-Quan Luo

Therefore, adversarial training for multiple perturbations (ATMP) is proposed to generalize the adversarial robustness over different perturbation types (in $\ell_1$, $\ell_2$, and $\ell_\infty$ norm-bounded perturbations).

Adversarial Robustness

A multi-domain VNE algorithm based on multi-objective optimization for IoD architecture in Industry 4.0

no code implementations8 Feb 2022 Peiying Zhang, Chao Wang, Zeyu Qin, Haotong Cao

Network virtualization technology is a promising technology to support IoD, so the allocation of virtual resources becomes a crucial issue in IoD.

Network Embedding

Random Noise Defense Against Query-Based Black-Box Attacks

1 code implementation NeurIPS 2021 Zeyu Qin, Yanbo Fan, Hongyuan Zha, Baoyuan Wu

We conduct the theoretical analysis about the effectiveness of RND against query-based black-box attacks and the corresponding adaptive attacks.

Adversarial Robustness

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