Search Results for author:
Found 9 papers, 6 papers with code
JailbreakBench: An Open Robustness Benchmark for Jailbreaking Large Language Models
To address these challenges, we introduce JailbreakBench, an open-sourced benchmark with the following components: (1) an evolving repository of state-of-the-art adversarial prompts, which we refer to as jailbreak artifacts; (2) a jailbreaking dataset comprising 100 behaviors -- both original and sourced from prior work -- which align with OpenAI's usage policies; (3) a standardized evaluation framework that includes a clearly defined threat model, system prompts, chat templates, and scoring functions; and (4) a leaderboard that tracks the performance of attacks and defenses for various LLMs.
A Safe Harbor for AI Evaluation and Red Teaming
Independent evaluation and red teaming are critical for identifying the risks posed by generative AI systems.
Jailbreaking Black Box Large Language Models in Twenty Queries
PAIR -- which is inspired by social engineering attacks -- uses an attacker LLM to automatically generate jailbreaks for a separate targeted LLM without human intervention.
Statistical Estimation Under Distribution Shift: Wasserstein Perturbations and Minimax Theory
Under a squared loss for mean estimation and prediction error in linear regression, we find the exact minimax risk, a least favorable perturbation, and show that the sample mean and least squares estimators are respectively optimal.
Black Box Adversarial Prompting for Foundation Models
Prompting interfaces allow users to quickly adjust the output of generative models in both vision and language.
Interventional and Counterfactual Inference with Diffusion Models
We consider the problem of answering observational, interventional, and counterfactual queries in a causally sufficient setting where only observational data and the causal graph are available.
AdaPT-GMM: Powerful and robust covariate-assisted multiple testing
We propose a new empirical Bayes method for covariate-assisted multiple testing with false discovery rate (FDR) control, where we model the local false discovery rate for each hypothesis as a function of both its covariates and p-value.
Generative Models for Pose Transfer
We investigate nearest neighbor and generative models for transferring pose between persons.
The Stochastic Replica Approach to Machine Learning: Stability and Parameter Optimization
We introduce a statistical physics inspired supervised machine learning algorithm for classification and regression problems.
