Assessing Adversarial Robustness of Large Language Models: An Empirical Study
<|reference_start|>Assessing Adversarial Robustness of Large Language Models: An Empirical Study: Large Language Models (LLMs) have revolutionized natural language processing, but their robustness against adversarial attacks remains a critical concern. We presents a novel white-box style attack approach that exposes vulnerabilities in leading open-source LLMs, including Llama, OPT, and T5. We assess the impact of model size, structure, and fine-tuning strategies on their resistance to adversarial perturbations. Our comprehensive evaluation across five diverse text classification tasks establishes a new benchmark for LLM robustness. The findings of this study have far-reaching implications for the reliable deployment of LLMs in real-world applications and contribute to the advancement of trustworthy AI systems.<|reference_end|>
@article{yang2024assessing,
    title={Assessing Adversarial Robustness of Large Language Models: An Empirical
  Study},
    author={Zeyu Yang, Zhao Meng, Xiaochen Zheng, Roger Wattenhofer},
    journal={arXiv preprint arXiv:2405.02764},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.02764},
    primaryClass={cs.CL cs.LG}
}