Adversarial Natural Language Processing: Overview, Challenges and Future Directions

Abstract

Natural language processing (NLP) has gained wider utilization with the emergence of large language models. However, adversarial attacks threaten their reliability. We present an overview of adversarial NLP with an emphasis on challenges, emerging areas and future directions. First, we review attack methods and evaluate the vulnerabilities of popular NLP models. Then, we review defense strategies including adversarial training. We identify key trends and suggest future directions such as the use of Bayesian methods to improve the security and robustness of NLP systems.