Ensuring Stability in Large Language Models: Countering Adversarial Attacks and Input Perturbations

Abstract

In recent years, large language models (LLMs) have demonstrated remarkable capabilities across various natural language processing tasks. However, their susceptibility to adversarial attacks and input perturbations poses significant challenges to their robustness and reliability. This paper presents an investigation into methods aimed at ensuring stability in LLMs by countering adversarial attacks and input perturbations. By addressing the critical issue of stability, this research contributes to the advancement of dependable and trustworthy large language models in real-world applications.