54 citations · 74 across the 15 of their papers we have counts for
7 papers · 1 filter
Like Oil and Water: Group Robustness Methods and Poisoning Defenses May Be at Odds
Michael-Andrei Panaitescu-Liess, Yigitcan Kaya, Sicheng Zhu +2
Group robustness has become a major concern in machine learning (ML) as conventional training paradigms were found to produce high error on minority groups. Without explicit group…
PoisonedParrot: Subtle Data Poisoning Attacks to Elicit Copyright-Infringing Content from Large Language Models
Michael-Andrei Panaitescu-Liess, Pankayaraj Pathmanathan, Yigitcan Kaya +5
As the capabilities of large language models (LLMs) continue to expand, their usage has become increasingly prevalent. However, as reflected in numerous ongoing lawsuits regarding…
AdvPrefix: An Objective for Nuanced LLM Jailbreaks
Sicheng Zhu, Brandon Amos, Yuandong Tian +2
Many jailbreak attacks on large language models (LLMs) rely on a common objective: making the model respond with the prefix ``Sure, here is (harmful request)''. While straightforwa…
Can Watermarking Large Language Models Prevent Copyrighted Text Generation and Hide Training Data?
Michael-Andrei Panaitescu-Liess, Zora Che, Bang An +6
Large Language Models (LLMs) have demonstrated impressive capabilities in generating diverse and contextually rich text. However, concerns regarding copyright infringement arise as…
Understanding the Generalization Benefit of Model Invariance from a Data Perspective
Sicheng Zhu, Bang An, Furong Huang
Machine learning models that are developed with invariance to certain types of data transformations have demonstrated superior generalization performance in practice. However, the…
Learning Adversarially Robust Representations via Worst-Case Mutual Information Maximization
Sicheng Zhu, Xiao Zhang, David Evans
Training machine learning models that are robust against adversarial inputs poses seemingly insurmountable challenges. To better understand adversarial robustness, we consider the…
Guess First to Enable Better Compression and Adversarial Robustness
Sicheng Zhu, Bang An, Shiyu Niu
Machine learning models are generally vulnerable to adversarial examples, which is in contrast to the robustness of humans. In this paper, we try to leverage one of the mechanisms…