Preserving the Integrity of Large Language Models: Strategies Against Adversarial Attacks and Input Distortions

Abstract

Large language models (LLMs) have demonstrated unprecedented performance across diverse natural language processing tasks, yet their vulnerability to adversarial attacks and input distortions raises concerns about their integrity and reliability. This paper investigates strategies for preserving the integrity of LLMs by mitigating adversarial attacks and input distortions. By addressing the crucial issue of integrity preservation, this research contributes to the development of trustworthy and dependable large language models for real-world applications.