Assessing Adversarial Robustness of Large Language Models: An Empirical Study
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2024
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866917774877523968 |
|---|---|
| author | Yang, Zeyu Meng, Zhao Zheng, Xiaochen Wattenhofer, Roger |
| author_facet | Yang, Zeyu Meng, Zhao Zheng, Xiaochen Wattenhofer, Roger |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_02764 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Assessing Adversarial Robustness of Large Language Models: An Empirical Study Yang, Zeyu Meng, Zhao Zheng, Xiaochen Wattenhofer, Roger Computation and Language Machine Learning 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. |
| title | Assessing Adversarial Robustness of Large Language Models: An Empirical Study |
| topic | Computation and Language Machine Learning |
| url | https://arxiv.org/abs/2405.02764 |