8 citations · 17 across the 14 of their papers we have counts for
14 papers
SEP-Attack: A Simple and Effective Paradigm for Transfer-Based Textual Adversarial Attack
Han Liu, Zhi Xu, Xiaotong Zhang +5
Despite the strong performance of deep neural networks in modern Web and language applications, they remain vulnerable to adversarial attacks, especially transferable attacks that…
HQA-VLAttack: Towards High Quality Adversarial Attack on Vision-Language Pre-Trained Models
Han Liu, Jiaqi Li, Zhi Xu +5
Black-box adversarial attack on vision-language pre-trained models is a practical and challenging task, as text and image perturbations need to be considered simultaneously, and on…
SEPTQ: A Simple and Effective Post-Training Quantization Paradigm for Large Language Models
Han Liu, Haotian Gao, Xiaotong Zhang +5
Large language models (LLMs) have shown remarkable performance in various domains, but they are constrained by massive computational and storage costs. Quantization, an effective t…
RUQuant: Towards Refining Uniform Quantization for Large Language Models
Han Liu, Haotian Gao, Changya Li +4
The increasing size and complexity of large language models (LLMs) have raised significant challenges in deployment efficiency, particularly under resource constraints. Post-traini…
TAO-Attack: Toward Advanced Optimization-Based Jailbreak Attacks for Large Language Models
Zhi Xu, Jiaqi Li, Xiaotong Zhang +2
Large language models (LLMs) have achieved remarkable success across diverse applications but remain vulnerable to jailbreak attacks, where attackers craft prompts that bypass safe…
Liberating Seen Classes: Boosting Few-Shot and Zero-Shot Text Classification via Anchor Generation and Classification Reframing
Han Liu, Siyang Zhao, Xiaotong Zhang +6
Few-shot and zero-shot text classification aim to recognize samples from novel classes with limited labeled samples or no labeled samples at all. While prevailing methods have show…
HQA-Attack: Toward High Quality Black-Box Hard-Label Adversarial Attack on Text
Han Liu, Zhi Xu, Xiaotong Zhang +5
Black-box hard-label adversarial attack on text is a practical and challenging task, as the text data space is inherently discrete and non-differentiable, and only the predicted la…
Boosting Decision-Based Black-Box Adversarial Attack with Gradient Priors
Han Liu, Xingshuo Huang, Xiaotong Zhang +6
Decision-based methods have shown to be effective in black-box adversarial attacks, as they can obtain satisfactory performance and only require to access the final model predictio…
Boosting Few-Shot Text Classification via Distribution Estimation
Han Liu, Feng Zhang, Xiaotong Zhang +6
Distribution estimation has been demonstrated as one of the most effective approaches in dealing with few-shot image classification, as the low-level patterns and underlying repres…
Label-enhanced Prototypical Network with Contrastive Learning for Multi-label Few-shot Aspect Category Detection
Han Liu, Feng Zhang, Xiaotong Zhang +4
Multi-label aspect category detection allows a given review sentence to contain multiple aspect categories, which is shown to be more practical in sentiment analysis and attracting…
A Simple Meta-learning Paradigm for Zero-shot Intent Classification with Mixture Attention Mechanism
Han Liu, Siyang Zhao, Xiaotong Zhang +4
Zero-shot intent classification is a vital and challenging task in dialogue systems, which aims to deal with numerous fast-emerging unacquainted intents without annotated training…
An Explicit-Joint and Supervised-Contrastive Learning Framework for Few-Shot Intent Classification and Slot Filling
Han Liu, Feng Zhang, Xiaotong Zhang +2
Intent classification (IC) and slot filling (SF) are critical building blocks in task-oriented dialogue systems. These two tasks are closely-related and can flourish each other. Si…
Clustering Uncertain Data via Representative Possible Worlds with Consistency Learning
Han Liu, Xianchao Zhang, Xiaotong Zhang +2
Clustering uncertain data is an essential task in data mining for the internet of things. Possible world based algorithms seem promising for clustering uncertain data. However, the…
Dimensionwise Separable 2-D Graph Convolution for Unsupervised and Semi-Supervised Learning on Graphs
Qimai Li, Xiaotong Zhang, Han Liu +2
Graph convolutional neural networks (GCN) have been the model of choice for graph representation learning, which is mainly due to the effective design of graph convolution that com…