84 citations · 120 across the 10 of their papers we have counts for
4 papers · 1 filter
An LLM can Fool Itself: A Prompt-Based Adversarial Attack
Xilie Xu, Keyi Kong, Ning Liu +4
The wide-ranging applications of large language models (LLMs), especially in safety-critical domains, necessitate the proper evaluation of the LLM's adversarial robustness. This pa…
AutoLoRa: A Parameter-Free Automated Robust Fine-Tuning Framework
Xilie Xu, Jingfeng Zhang, Mohan Kankanhalli
Robust Fine-Tuning (RFT) is a low-cost strategy to obtain adversarial robustness in downstream applications, without requiring a lot of computational resources and collecting signi…
Enhancing Adversarial Contrastive Learning via Adversarial Invariant Regularization
Xilie Xu, Jingfeng Zhang, Feng Liu +2
Adversarial contrastive learning (ACL) is a technique that enhances standard contrastive learning (SCL) by incorporating adversarial data to learn a robust representation that can…
Efficient Adversarial Contrastive Learning via Robustness-Aware Coreset Selection
Xilie Xu, Jingfeng Zhang, Feng Liu +2
Adversarial contrastive learning (ACL) does not require expensive data annotations but outputs a robust representation that withstands adversarial attacks and also generalizes to a…