28 citations · 105 across the 18 of their papers we have counts for
18 papers
NAP^2: A Benchmark for Naturalness and Privacy-Preserving Text Rewriting by Learning from Human
Shuo Huang, William MacLean, Xiaoxi Kang +5
The widespread use of cloud-based Large Language Models (LLMs) has heightened concerns over user privacy, as sensitive information may be inadvertently exposed during interactions…
Attacks on Third-Party APIs of Large Language Models
Wanru Zhao, Vidit Khazanchi, Haodi Xing +3
Large language model (LLM) services have recently begun offering a plugin ecosystem to interact with third-party API services. This innovation enhances the capabilities of LLMs, bu…
Generative Models are Self-Watermarked: Declaring Model Authentication through Re-Generation
Aditya Desu, Xuanli He, Qiongkai Xu +1
As machine- and AI-generated content proliferates, protecting the intellectual property of generative models has become imperative, yet verifying data ownership poses formidable ch…
Here's a Free Lunch: Sanitizing Backdoored Models with Model Merge
Ansh Arora, Xuanli He, Maximilian Mozes +3
The democratization of pre-trained language models through open-source initiatives has rapidly advanced innovation and expanded access to cutting-edge technologies. However, this o…
G3Detector: General GPT-Generated Text Detector
Haolan Zhan, Xuanli He, Qiongkai Xu +2
The burgeoning progress in the field of Large Language Models (LLMs) heralds significant benefits due to their unparalleled capacities. However, it is critical to acknowledge the p…
Mitigating Backdoor Poisoning Attacks through the Lens of Spurious Correlation
Xuanli He, Qiongkai Xu, Jun Wang +2
Modern NLP models are often trained over large untrusted datasets, raising the potential for a malicious adversary to compromise model behaviour. For instance, backdoors can be imp…
Training-free Lexical Backdoor Attacks on Language Models
Yujin Huang, Terry Yue Zhuo, Qiongkai Xu +3
Large-scale language models have achieved tremendous success across various natural language processing (NLP) applications. Nevertheless, language models are vulnerable to backdoor…
Extracted BERT Model Leaks More Information than You Think!
Xuanli He, Chen Chen, Lingjuan Lyu +1
The collection and availability of big data, combined with advances in pre-trained models (e.g. BERT), have revolutionized the predictive performance of natural language processing…
CATER: Intellectual Property Protection on Text Generation APIs via Conditional Watermarks
Xuanli He, Qiongkai Xu, Yi Zeng +4
Previous works have validated that text generation APIs can be stolen through imitation attacks, causing IP violations. In order to protect the IP of text generation APIs, a recent…
Rethinking Round-Trip Translation for Machine Translation Evaluation
Terry Yue Zhuo, Qiongkai Xu, Xuanli He +1
Automatic evaluation on low-resource language translation suffers from a deficiency of parallel corpora. Round-trip translation could be served as a clever and straightforward tech…
Variational Autoencoder with Disentanglement Priors for Low-Resource Task-Specific Natural Language Generation
Zhuang Li, Lizhen Qu, Qiongkai Xu +3
In this paper, we propose a variational autoencoder with disentanglement priors, VAE-DPRIOR, for task-specific natural language generation with none or a handful of task-specific l…
Protecting Intellectual Property of Language Generation APIs with Lexical Watermark
Xuanli He, Qiongkai Xu, Lingjuan Lyu +2
Nowadays, due to the breakthrough in natural language generation (NLG), including machine translation, document summarization, image captioning, etc NLG models have been encapsulat…
Humanly Certifying Superhuman Classifiers
Qiongkai Xu, Christian Walder, Chenchen Xu
Estimating the performance of a machine learning system is a longstanding challenge in artificial intelligence research. Today, this challenge is especially relevant given the emer…
Student Surpasses Teacher: Imitation Attack for Black-Box NLP APIs
Qiongkai Xu, Xuanli He, Lingjuan Lyu +2
Machine-learning-as-a-service (MLaaS) has attracted millions of users to their splendid large-scale models. Although published as black-box APIs, the valuable models behind these s…
Model Extraction and Adversarial Transferability, Your BERT is Vulnerable!
Xuanli He, Lingjuan Lyu, Qiongkai Xu +1
Natural language processing (NLP) tasks, ranging from text classification to text generation, have been revolutionised by the pre-trained language models, such as BERT. This allows…
ALTER: Auxiliary Text Rewriting Tool for Natural Language Generation
Qiongkai Xu, Chenchen Xu, Lizhen Qu
In this paper, we describe ALTER, an auxiliary text rewriting tool that facilitates the rewriting process for natural language generation tasks, such as paraphrasing, text simplifi…
D-PAGE: Diverse Paraphrase Generation
Qiongkai Xu, Juyan Zhang, Lizhen Qu +2
In this paper, we investigate the diversity aspect of paraphrase generation. Prior deep learning models employ either decoding methods or add random input noise for varying outputs…
Collective Vertex Classification Using Recursive Neural Network
Qiongkai Xu, Qing Wang, Chenchen Xu +1
Collective classification of vertices is a task of assigning categories to each vertex in a graph based on both vertex attributes and link structure. Nevertheless, some existing ap…