Guarding the Integrity of Large Language Models
Keywords:
Large Language Models, Robustness, Adversarial Attacks, Input Perturbations, Adversarial Training, Robust Optimization, Input Preprocessing, VulnerabilitiesAbstract
Large language models (LLMs) have exhibited remarkable capabilities in various natural language processing tasks. However, their integrity is threatened by adversarial attacks and variations in input data. This paper explores methods to guard the integrity of LLMs by countering adversarial threats and addressing input variations. By focusing on guarding LLM integrity, this research contributes to the development of robust and dependable large language models suitable for real-world applications. This paper explores strategies aimed at preserving the integrity of LLMs by countering adversarial threats and accommodating input variations. We survey existing techniques for enhancing LLM robustness and propose novel methods to mitigate adversarial threats and address input variations.
