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Found 7 papers, 6 papers with code
Calibrating Large Language Models Using Their Generations Only
As large language models (LLMs) are increasingly deployed in user-facing applications, building trust and maintaining safety by accurately quantifying a model's confidence in its prediction becomes even more important.
TRAP: Targeted Random Adversarial Prompt Honeypot for Black-Box Identification
Large Language Model (LLM) services and models often come with legal rules on who can use them and how they must use them.
Going Further: Flatness at the Rescue of Early Stopping for Adversarial Example Transferability
A common hypothesis to explain this is that deep neural networks (DNNs) first learn robust features, which are more generic, thus a better surrogate.
LGV: Boosting Adversarial Example Transferability from Large Geometric Vicinity
We propose transferability from Large Geometric Vicinity (LGV), a new technique to increase the transferability of black-box adversarial attacks.
Influence-Driven Data Poisoning in Graph-Based Semi-Supervised Classifiers
Graph-based Semi-Supervised Learning (GSSL) is a practical solution to learn from a limited amount of labelled data together with a vast amount of unlabelled data.
Efficient and Transferable Adversarial Examples from Bayesian Neural Networks
An established way to improve the transferability of black-box evasion attacks is to craft the adversarial examples on an ensemble-based surrogate to increase diversity.
Adversarial Perturbation Intensity Achieving Chosen Intra-Technique Transferability Level for Logistic Regression
Machine Learning models have been shown to be vulnerable to adversarial examples, ie.
