Improving the robustness of machine learning (ML) models for natural language tasks has become a major artificial intelligence (AI) topic in recent years. Large language models (LLMs) have always been one of the most trending areas in AI research, backed by the rise of generative AI and companies racing to release architectures that can create impressively readable content, even computer code. Language models have traditionally been trained using online texts from sources such as Wikipedia, news stories, scientific papers and novels. However, in recent years, the tendency has been to train these models on increasing amounts of data in order to improve their accuracy and versatility.
