1.8k citations · 9.5k across the 166 of their papers we have counts for
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Red Teaming Language Model Detectors with Language Models
Zhouxing Shi, Yihan Wang, Fan Yin +3
The prevalence and strong capability of large language models (LLMs) present significant safety and ethical risks if exploited by malicious users. To prevent the potentially decept…
Two-stage LLM Fine-tuning with Less Specialization and More Generalization
Yihan Wang, Si Si, Daliang Li +5
Pretrained large language models (LLMs) are general purpose problem solvers applicable to a diverse set of tasks with prompts. They can be further improved towards a specific task…
ADDMU: Detection of Far-Boundary Adversarial Examples with Data and Model Uncertainty Estimation
Fan Yin, Yao Li, Cho-Jui Hsieh +1
Adversarial Examples Detection (AED) is a crucial defense technique against adversarial attacks and has drawn increasing attention from the Natural Language Processing (NLP) commun…
Watermarking Pre-trained Language Models with Backdooring
Chenxi Gu, Chengsong Huang, Xiaoqing Zheng +2
Large pre-trained language models (PLMs) have proven to be a crucial component of modern natural language processing systems. PLMs typically need to be fine-tuned on task-specific…
Improving the Adversarial Robustness of NLP Models by Information Bottleneck
Cenyuan Zhang, Xiang Zhou, Yixin Wan +3
Existing studies have demonstrated that adversarial examples can be directly attributed to the presence of non-robust features, which are highly predictive, but can be easily manip…
Searching for an Effective Defender: Benchmarking Defense against Adversarial Word Substitution
Zongyi Li, Jianhan Xu, Jiehang Zeng +5
Recent studies have shown that deep neural networks are vulnerable to intentionally crafted adversarial examples, and various methods have been proposed to defend against adversari…
Double Perturbation: On the Robustness of Robustness and Counterfactual Bias Evaluation
Chong Zhang, Jieyu Zhao, Huan Zhang +2
Robustness and counterfactual bias are usually evaluated on a test dataset. However, are these evaluations robust? If the test dataset is perturbed slightly, will the evaluation re…
On the Sensitivity and Stability of Model Interpretations in NLP
Fan Yin, Zhouxing Shi, Cho-Jui Hsieh +1
Recent years have witnessed the emergence of a variety of post-hoc interpretations that aim to uncover how natural language processing (NLP) models make predictions. Despite the su…
Defense against Adversarial Attacks in NLP via Dirichlet Neighborhood Ensemble
Yi Zhou, Xiaoqing Zheng, Cho-Jui Hsieh +2
Despite neural networks have achieved prominent performance on many natural language processing (NLP) tasks, they are vulnerable to adversarial examples. In this paper, we propose…
Efficient Contextual Representation Learning Without Softmax Layer
Liunian Harold Li, Patrick H. Chen, Cho-Jui Hsieh +1
Contextual representation models have achieved great success in improving various downstream tasks. However, these language-model-based encoders are difficult to train due to the l…
GroupReduce: Block-Wise Low-Rank Approximation for Neural Language Model Shrinking
Patrick H. Chen, Si Si, Yang Li +2
Model compression is essential for serving large deep neural nets on devices with limited resources or applications that require real-time responses. As a case study, a state-of-th…
LearningWord Embeddings for Low-resource Languages by PU Learning
Chao Jiang, Hsiang-Fu Yu, Cho-Jui Hsieh +1
Word embedding is a key component in many downstream applications in processing natural languages. Existing approaches often assume the existence of a large collection of text for…