28 citations · 105 across the 18 of their papers we have counts for
9 papers · 1 filter
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…
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…
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…
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…