Defending the Robustness of Large Language Models: Mitigating Adversarial Threats and Input Variability

Abstract

The robustness of large language models (LLMs) against adversarial threats and input variability is crucial for their reliable deployment in real-world applications. This paper investigates strategies for defending the robustness of LLMs by mitigating adversarial threats and addressing input variability. We survey existing approaches for enhancing LLM robustness and propose novel methods to counter adversarial threats and accommodate input variability. By focusing on defending LLM robustness, this research contributes to the development of dependable and trustworthy large language models suitable for diverse application domains.