<ul><li>The rise of Large Language Models (LLMs) has revolutionized artificial intelligence, but they are vulnerable to adversarial perturbations.</li><li>The Robustness Measurement and Assessment (RoMA) framework is adapted to quantify LLM resilience against adversarial inputs.</li><li>RoMA's estimates demonstrate accuracy with minimal error margins and computational efficiency.</li><li>LLM robustness varies between models, categories within the same task, and types of perturbations, highlighting the need for task-specific evaluations.</li></ul>

Towards Robust LLMs: an Adversarial Robustness Measurement Framework

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