Search Results for author:
Found 9 papers, 8 papers with code
On the Duality Between Sharpness-Aware Minimization and Adversarial Training
Instead of perturbing the samples, Sharpness-Aware Minimization (SAM) perturbs the model weights during training to find a more flat loss landscape and improve generalization.
Studious Bob Fight Back Against Jailbreaking via Prompt Adversarial Tuning
To our knowledge, we are the first to implement defense from the perspective of prompt tuning.
Jatmo: Prompt Injection Defense by Task-Specific Finetuning
Jatmo only needs a task prompt and a dataset of inputs for the task: it uses the teacher model to generate outputs.
Jailbreak and Guard Aligned Language Models with Only Few In-Context Demonstrations
Large Language Models (LLMs) have shown remarkable success in various tasks, but concerns about their safety and the potential for generating malicious content have emerged.
Weighted Automata Extraction and Explanation of Recurrent Neural Networks for Natural Language Tasks
In this paper, we propose a novel framework of Weighted Finite Automata (WFA) extraction and explanation to tackle the limitations for natural language tasks.
Sharpness-Aware Minimization Alone can Improve Adversarial Robustness
In this paper, we explore SAM in the context of adversarial robustness.
Using Z3 for Formal Modeling and Verification of FNN Global Robustness
To evaluate the effectiveness of our implementation and improvements, we conduct extensive experiments on a set of benchmark datasets.
CFA: Class-wise Calibrated Fair Adversarial Training
Adversarial training has been widely acknowledged as the most effective method to improve the adversarial robustness against adversarial examples for Deep Neural Networks (DNNs).
Extracting Weighted Finite Automata from Recurrent Neural Networks for Natural Languages
Compositional approaches that are scablable to natural languages fall short in extraction precision.
