Adversarial Natural Language

Adversarial natural language processing (NLP) focuses on creating and detecting subtly altered text inputs designed to fool NLP models, revealing vulnerabilities and biases. Current research emphasizes developing adversarial datasets in various languages and for different NLP tasks (e.g., natural language inference, code understanding), often leveraging large language models to generate challenging examples and exploring model-agnostic detection methods based on analyzing model outputs. This work is crucial for improving the robustness and reliability of NLP systems, ultimately leading to more secure and trustworthy applications.