ASSERT: Automated Safety Scenario Red Teaming for Evaluating the Robustness of Large Language Models
Alex Mei, Sharon Levy, William Yang Wang · 2023
As large language models are integrated into society, robustness toward a suite of prompts is increasingly important to maintain reliability in a high-variance environment.Robustness evaluations must comprehensively encapsulate the various settings in which a user may invoke an intelligent system.This paper proposes ASSERT, Automated Safety ScEnario Red Teaming, consisting of three methods -semantically aligned augmentation, target bootstrapping, and adversarial knowledge injection.For robust safety evaluation, we apply these methods in the critical domain of AI safety to algorithmically generate a test suite of prompts covering diverse robustness settings -semantic equivalence, related scenarios, and adversarial.We partition our prompts into four safety domains for a fine-grained analysis of how the domain affects model performance.Despite dedicated safeguards in existing state-of-the-art models, we find statistically significant performance differences of up to 11% in absolute classification accuracy among semantically related scenarios and error rates of up to 19% absolute error in zero-shot adversarial settings, raising concerns for users' physical safety.